modelId
string
author
string
last_modified
timestamp[us, tz=UTC]
downloads
int64
likes
int64
library_name
string
tags
list
pipeline_tag
string
createdAt
timestamp[us, tz=UTC]
card
string
Kansallisarkisto/multicentury-htr-model
Kansallisarkisto
2025-06-25T05:28:51Z
33
0
null
[ "pytorch", "vision-encoder-decoder", "image-to-text", "fi", "sv", "license:apache-2.0", "region:us" ]
image-to-text
2024-10-07T11:48:19Z
--- license: apache-2.0 language: - fi - sv metrics: - cer pipeline_tag: image-to-text --- # Model description **Model Name:** multicentury-htr-model **Model Type:** Transformer-based OCR (TrOCR) **Base Model:** microsoft/trocr-large-handwritten **Purpose:** Handwritten text recognition **Languages:** Swedish, Finnish **License:** Apache 2.0 This model is a fine-tuned version of the microsoft/trocr-large-handwritten model, specialized for recognizing handwritten text. It has been trained on various dataset from 17th to 20th centuries and can be used for applications such as document digitization, form recognition, or any task involving handwritten text extraction. # Model Architecture The model is based on a Transformer architecture (TrOCR) with an encoder-decoder setup: - The encoder processes images of handwritten text. - The decoder generates corresponding text output. # Intended Use This model is designed for handwritten text recognition and is intended for use in: - Document digitization (e.g., archival work, historical manuscripts) - Handwritten notes transcription # Training data The training datasetincludes more than 760 000 samples of handwritten text rows, covering a wide variety of handwriting styles and text samples. # Evaluation The model was evaluated on test dataset. Below are key metrics: **Character Error Rate (CER):** 3.2 **Test Dataset Description:** size ~94 900 text rows # Used Hyperparameters **Evaluation strategy:** epoch **Train batch size per device:** 16 **Learning rate:** 1e-5 **Scheduler:** linear **Warmup steps:** 500 **Optimizer:** AdamW **Number of epochs:** 14 **FP16 mixed precision training:** True **Half precision backend:** cuda_amp # How to Use the Model You can use the model directly with Hugging Face’s pipeline function or by manually loading the processor and model. ```python from transformers import TrOCRProcessor, VisionEncoderDecoderModel from PIL import Image # Load the model and processor processor = TrOCRProcessor.from_pretrained("Kansallisarkisto/multicentury-htr-model/processor") model = VisionEncoderDecoderModel.from_pretrained("Kansallisarkisto/multicentury-htr-model") # Open an image of handwritten text image = Image.open("path_to_image.png") # Preprocess and predict pixel_values = processor(image, return_tensors="pt").pixel_values generated_ids = model.generate(pixel_values) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] print(generated_text) ``` # Limitations and Biases The model was trained primarily on handwritten text that uses basic Latin characters (A-Z, a-z) and includes Nordic special characters (å, ä, ö). It has not been trained on non-Latin alphabets, such as Chinese characters, Cyrillic script, or other writing systems like Arabic or Hebrew. The model may not generalize well to any other languages than Finnish, Swedish or English. # Future Work Potential improvements for this model include: - Expanding training data: Incorporating more diverse handwriting styles and languages. - Optimizing for specific domains: Fine-tuning the model on domain-specific handwriting. # Citation If you use this model in your work, please cite it as: @misc{multicentury_htr_model_2024, author = {Kansallisarkisto}, title = {Multicentury HTR Model: Handwritten Text Recognition}, year = {2024}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Kansallisarkisto/multicentury-htr-model/}}, } ## Model Card Authors Author: Kansallisarkisto Contact Information: [email protected], [email protected]
nurulfauzh/sentimen-analisis-model
nurulfauzh
2025-06-25T05:11:25Z
0
0
null
[ "tf", "bert", "license:apache-2.0", "region:us" ]
null
2025-06-25T04:58:09Z
--- license: apache-2.0 ---
New-videos-18-Bu-Guru-Salsa-Viral-Video/FULL.VIDEO.Bu.Guru.Salsa.Viral.Video.Tutorial.Official
New-videos-18-Bu-Guru-Salsa-Viral-Video
2025-06-25T04:32:57Z
0
0
null
[ "region:us" ]
null
2025-06-25T04:32:43Z
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
pennylin09/uuu_fine_tune_gpt2
pennylin09
2025-06-25T03:38:19Z
0
0
null
[ "safetensors", "gpt2", "license:apache-2.0", "region:us" ]
null
2025-06-25T02:37:31Z
--- license: apache-2.0 ---
John6666/xl-sat-ior-imitation-of-reality-xl-sat-iorv1-sdxl
John6666
2025-06-25T03:27:37Z
0
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "photoreal", "real", "sateluco", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]
text-to-image
2025-06-25T03:21:51Z
--- license: creativeml-openrail-m language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic - photoreal - real - sateluco --- Original model is [here](https://civitai.com/models/1713256/xl-sat-ior-imitation-of-reality?modelVersionId=1938771). This model created by [Sateluco](https://civitai.com/user/Sateluco).
JFernandoGRE/llama31_8b_augmenteddemocracy_dpo2_questions_50_critsupport
JFernandoGRE
2025-06-25T02:44:52Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport", "base_model:finetune:JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2025-06-25T02:39:47Z
--- base_model: JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** JFernandoGRE - **License:** apache-2.0 - **Finetuned from model :** JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
yashmahe2018/math-error-classification-gguf
yashmahe2018
2025-06-25T01:58:19Z
0
0
transformers
[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-06-25T01:57:48Z
--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** yashmahe2018 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
VarunNagaraj/tiny-llm-maui-apiclients-mistral
VarunNagaraj
2025-06-25T01:01:30Z
0
0
transformers
[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-instruct-v0.1-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-instruct-v0.1-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-06-25T01:00:24Z
--- base_model: unsloth/mistral-7b-instruct-v0.1-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** VarunNagaraj - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-instruct-v0.1-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX
mlx-community
2025-06-25T00:51:02Z
0
0
mlx
[ "mlx", "safetensors", "mixtral", "Llama 3.2", "8 X 4B", "Brainstorm 5x", "128k context", "moe", "8 experts", "mixture of experts", "fine tune", "text-generation", "conversational", "base_model:DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit", "base_model:quantized:DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit", "8-bit", "region:us" ]
text-generation
2025-06-25T00:35:40Z
--- library_name: mlx tags: - Llama 3.2 - 8 X 4B - Brainstorm 5x - 128k context - moe - 8 experts - mixture of experts - fine tune - mlx base_model: DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit pipeline_tag: text-generation --- # mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX This model [mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX](https://huggingface.co/mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX) was converted to MLX format from [DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit](https://huggingface.co/DavidAU/L3.2-8X4B-MOE-V2-Dark-Champion-Inst-21B-uncen-ablit) using mlx-lm version **0.25.2**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Llama-3.2-8X4B-MOE-V2-Dark-Champion-Instruct-uncensored-abliterated-21B-MLX") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
pankajrajdeo/CT-UMLS-Summarizer-Qwen2.5-3B-4bit-adapter
pankajrajdeo
2025-06-24T23:53:35Z
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-06-24T23:53:22Z
--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** pankajrajdeo - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
18-videos-jobz-hunting-sajal-malik-virals/FULL.VIDEO.sajal.malik.Viral.Video.Tutorial.Official
18-videos-jobz-hunting-sajal-malik-virals
2025-06-24T23:23:06Z
0
0
null
[ "region:us" ]
null
2025-06-24T23:22:49Z
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
yu3733/paligemma2-3b-lora-vqa-v21-enhanced-d8000-r16
yu3733
2025-06-24T22:29:33Z
0
0
peft
[ "peft", "safetensors", "paligemma", "lora", "adapter", "visual-question-answering", "image-to-text", "v2.1-enhanced", "en", "base_model:google/paligemma2-3b-mix-224", "base_model:adapter:google/paligemma2-3b-mix-224", "region:us" ]
image-to-text
2025-06-24T22:29:19Z
--- tags: - paligemma - lora - adapter - visual-question-answering - image-to-text - v2.1-enhanced base_model: google/paligemma2-3b-mix-224 language: - en library_name: peft --- # paligemma2-3b-lora-vqa-v21-enhanced-d8000-r16 - v2.1 Enhanced This is a **v2.1 Enhanced** LoRA adapter for PaliGemma-2 3B trained on VQA tasks. ## 🆕 v2.1 Enhanced Improvements - **EOS Token Learning**: Explicit EOS tokens for better generation termination - **Memory Optimization**: 16-step gradient accumulation for stability - **VizWiz Format Support**: Full support with most frequent answer selection - **Robust Label Masking**: Enhanced prompt masking during training - **Production Memory Management**: Advanced garbage collection ## Usage ```python from transformers import AutoProcessor, PaliGemmaForConditionalGeneration from peft import PeftModel import torch from PIL import Image # Base model base_model_id = "google/paligemma2-3b-mix-224" adapter_id = "yu3733/paligemma2-3b-lora-vqa-v21-enhanced-d8000-r16" # Load processor processor = AutoProcessor.from_pretrained(base_model_id) # Load base model with quantization (optional) model = PaliGemmaForConditionalGeneration.from_pretrained( base_model_id, torch_dtype=torch.float16, device_map="auto" ) # Load LoRA adapter model = PeftModel.from_pretrained(model, adapter_id) # Prepare input image = Image.open("your_image.jpg") prompt = "<image>\nQuestion: What is in this image?\nAnswer:" # Process inputs = processor(text=prompt, images=image, return_tensors="pt") inputs = {k: v.to(model.device) for k, v in inputs.items()} # Generate with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=20) # Decode print(processor.decode(outputs[0], skip_special_tokens=True)) ``` ## Training Configuration - **Base Model**: google/paligemma2-3b-mix-224 - **LoRA Rank**: 16 - **Training Framework**: PEFT + Transformers - **Optimization**: 4-bit quantization + gradient checkpointing - **Dataset**: VizWiz VQA ## License Same as the base model (see google/paligemma2-3b-mix-224)
rbelanec/train_qnli_1750781358
rbelanec
2025-06-24T22:14:42Z
0
0
peft
[ "peft", "safetensors", "llama-factory", "prompt-tuning", "generated_from_trainer", "base_model:google/gemma-3-1b-it", "base_model:adapter:google/gemma-3-1b-it", "license:gemma", "region:us" ]
null
2025-06-24T16:11:43Z
--- library_name: peft license: gemma base_model: google/gemma-3-1b-it tags: - llama-factory - prompt-tuning - generated_from_trainer model-index: - name: train_qnli_1750781358 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # train_qnli_1750781358 This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on the qnli dataset. It achieves the following results on the evaluation set: - Loss: 0.0861 - Num Input Tokens Seen: 117444992 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 2 - seed: 123 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF
bartowski
2025-06-24T21:56:58Z
5,846
16
null
[ "gguf", "image-text-to-text", "en", "fr", "de", "es", "pt", "it", "ja", "ko", "ru", "zh", "ar", "fa", "id", "ms", "ne", "pl", "ro", "sr", "sv", "tr", "uk", "vi", "hi", "bn", "base_model:mistralai/Mistral-Small-3.2-24B-Instruct-2506", "base_model:quantized:mistralai/Mistral-Small-3.2-24B-Instruct-2506", "license:apache-2.0", "region:us", "conversational" ]
image-text-to-text
2025-06-20T19:03:22Z
--- quantized_by: bartowski pipeline_tag: image-text-to-text language: - en - fr - de - es - pt - it - ja - ko - ru - zh - ar - fa - id - ms - ne - pl - ro - sr - sv - tr - uk - vi - hi - bn base_model: mistralai/Mistral-Small-3.2-24B-Instruct-2506 base_model_relation: quantized license: apache-2.0 inference: false extra_gated_description: >- If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>. --- ## Llamacpp imatrix Quantizations of Mistral-Small-3.2-24B-Instruct-2506 by mistralai Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5697">b5697</a> for quantization. Original model: https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506 All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) Run them in [LM Studio](https://lmstudio.ai/) Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project ## Prompt format ``` <s>[SYSTEM_PROMPT]{system_prompt}[/SYSTEM_PROMPT][INST]{prompt}[/INST] ``` ## What's new: Fix chat template to support tool calling Will require use of --chat-template and the Mistral-Small-3.2-24B-Instruct-2506.jinja, uploaded here and available in llama.cpp (if the PR is merged: https://github.com/ggml-org/llama.cpp/pull/14349) Full server run command: ``` ./llama-server -m /models/Mistral-Small-3.2-24B-Instruct-2506-GGUF/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf -ngl 100 --host 0.0.0.0 -fa --slots --jinja --chat-template-file /models/Mistral-Small-3.2-24B-Instruct-2506.jinja ``` ## Download a file (not the whole branch) from below: | Filename | Quant type | File Size | Split | Description | | -------- | ---------- | --------- | ----- | ----------- | | [Mistral-Small-3.2-24B-Instruct-2506-bf16.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-bf16.gguf) | bf16 | 47.15GB | false | Full BF16 weights. | | [Mistral-Small-3.2-24B-Instruct-2506-Q8_0.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q8_0.gguf) | Q8_0 | 25.05GB | false | Extremely high quality, generally unneeded but max available quant. | | [Mistral-Small-3.2-24B-Instruct-2506-Q6_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q6_K_L.gguf) | Q6_K_L | 19.67GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. | | [Mistral-Small-3.2-24B-Instruct-2506-Q6_K.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q6_K.gguf) | Q6_K | 19.35GB | false | Very high quality, near perfect, *recommended*. | | [Mistral-Small-3.2-24B-Instruct-2506-Q5_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q5_K_L.gguf) | Q5_K_L | 17.18GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. | | [Mistral-Small-3.2-24B-Instruct-2506-Q5_K_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q5_K_M.gguf) | Q5_K_M | 16.76GB | false | High quality, *recommended*. | | [Mistral-Small-3.2-24B-Instruct-2506-Q5_K_S.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q5_K_S.gguf) | Q5_K_S | 16.30GB | false | High quality, *recommended*. | | [Mistral-Small-3.2-24B-Instruct-2506-Q4_1.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_1.gguf) | Q4_1 | 14.87GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. | | [Mistral-Small-3.2-24B-Instruct-2506-Q4_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_L.gguf) | Q4_K_L | 14.83GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. | | [Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf) | Q4_K_M | 14.33GB | false | Good quality, default size for most use cases, *recommended*. | | [Mistral-Small-3.2-24B-Instruct-2506-Q4_K_S.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_S.gguf) | Q4_K_S | 13.55GB | false | Slightly lower quality with more space savings, *recommended*. | | [Mistral-Small-3.2-24B-Instruct-2506-Q4_0.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_0.gguf) | Q4_0 | 13.49GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. | | [Mistral-Small-3.2-24B-Instruct-2506-IQ4_NL.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ4_NL.gguf) | IQ4_NL | 13.47GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. | | [Mistral-Small-3.2-24B-Instruct-2506-Q3_K_XL.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q3_K_XL.gguf) | Q3_K_XL | 12.99GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. | | [Mistral-Small-3.2-24B-Instruct-2506-IQ4_XS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ4_XS.gguf) | IQ4_XS | 12.76GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [Mistral-Small-3.2-24B-Instruct-2506-Q3_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q3_K_L.gguf) | Q3_K_L | 12.40GB | false | Lower quality but usable, good for low RAM availability. | | [Mistral-Small-3.2-24B-Instruct-2506-Q3_K_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q3_K_M.gguf) | Q3_K_M | 11.47GB | false | Low quality. | | [Mistral-Small-3.2-24B-Instruct-2506-IQ3_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ3_M.gguf) | IQ3_M | 10.65GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [Mistral-Small-3.2-24B-Instruct-2506-Q3_K_S.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q3_K_S.gguf) | Q3_K_S | 10.40GB | false | Low quality, not recommended. | | [Mistral-Small-3.2-24B-Instruct-2506-IQ3_XS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ3_XS.gguf) | IQ3_XS | 9.91GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | [Mistral-Small-3.2-24B-Instruct-2506-Q2_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q2_K_L.gguf) | Q2_K_L | 9.55GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. | | [Mistral-Small-3.2-24B-Instruct-2506-IQ3_XXS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ3_XXS.gguf) | IQ3_XXS | 9.28GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. | | [Mistral-Small-3.2-24B-Instruct-2506-Q2_K.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q2_K.gguf) | Q2_K | 8.89GB | false | Very low quality but surprisingly usable. | | [Mistral-Small-3.2-24B-Instruct-2506-IQ2_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ2_M.gguf) | IQ2_M | 8.11GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. | | [Mistral-Small-3.2-24B-Instruct-2506-IQ2_S.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ2_S.gguf) | IQ2_S | 7.48GB | false | Low quality, uses SOTA techniques to be usable. | | [Mistral-Small-3.2-24B-Instruct-2506-IQ2_XS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ2_XS.gguf) | IQ2_XS | 7.21GB | false | Low quality, uses SOTA techniques to be usable. | | [Mistral-Small-3.2-24B-Instruct-2506-IQ2_XXS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ2_XXS.gguf) | IQ2_XXS | 6.55GB | false | Very low quality, uses SOTA techniques to be usable. | ## Embed/output weights Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. ## Downloading using huggingface-cli <details> <summary>Click to view download instructions</summary> First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF --include "mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF --include "mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q8_0/*" --local-dir ./ ``` You can either specify a new local-dir (mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q8_0) or download them all in place (./) </details> ## ARM/AVX information Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass. Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly. As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0. Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase. <details> <summary>Click to view Q4_0_X_X information (deprecated</summary> I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking. <details> <summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary> | model | size | params | backend | threads | test | t/s | % (vs Q4_0) | | ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: | | qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% | | qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% | | qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% | | qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% | | qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% | | qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% | | qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% | | qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% | | qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% | | qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% | | qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% | | qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% | | qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% | | qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% | | qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% | | qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% | | qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% | | qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% | Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation </details> </details> ## Which file should I choose? <details> <summary>Click here for details</summary> A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. </details> ## Credits Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset. Thank you ZeroWw for the inspiration to experiment with embed/output. Thank you to LM Studio for sponsoring my work. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
tekeufranck681/lung-tumor-detection-model
tekeufranck681
2025-06-24T21:10:42Z
0
0
null
[ "region:us" ]
null
2025-06-24T16:42:09Z
# Lung Cancer Detection Inference Endpoint (with Grad-CAM) This endpoint accepts a base64-encoded lung image and returns: - Predicted cancer type - Confidence - Probabilities for all classes - Grad-CAM annotated image (base64) ### Expected Input ```json { "image": "BASE64_ENCODED_JPEG_IMAGE" }
New-videos-Maya-G-viral-video-Clips/FULL.VIDEO.Maya.G.Viral.Video.Tutorial.Official
New-videos-Maya-G-viral-video-Clips
2025-06-24T20:55:21Z
0
0
null
[ "region:us" ]
null
2025-06-24T20:55:05Z
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-up_positive-negative-addition-same_layer_20_2_song_3_49
winnieyangwannan
2025-06-24T20:39:13Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-24T05:44:29Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
SrivatsaBhamidipati/starcoder2-3b-qlora
SrivatsaBhamidipati
2025-06-24T20:22:59Z
0
0
peft
[ "peft", "safetensors", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:adapter:codellama/CodeLlama-7b-Instruct-hf", "license:llama2", "region:us" ]
null
2025-06-24T19:12:17Z
--- library_name: peft license: llama2 base_model: codellama/CodeLlama-7b-Instruct-hf tags: - generated_from_trainer model-index: - name: starcoder2-3b-qlora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # starcoder2-3b-qlora This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.14.0 - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Esabramowitz/Taxi-v3
Esabramowitz
2025-06-24T18:20:58Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2025-06-24T18:20:51Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.48 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Esabramowitz/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
jetfan-xin/ppo-Pyramids
jetfan-xin
2025-06-24T17:32:47Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2025-06-24T17:19:42Z
--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: jetfan-xin/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀 # 🧠 PPO Agent Trained on Unity Pyramids Environment This repository contains a reinforcement learning agent trained using **Proximal Policy Optimization (PPO)** on Unity’s **Pyramids** environment via **ML-Agents**. ## 📌 Model Overview - **Algorithm**: PPO with RND (Random Network Distillation) - **Environment**: Unity Pyramids (3D sparse-reward maze) - **Framework**: ML-Agents v1.2.0.dev0 - **Backend**: PyTorch 2.7.1 (CUDA-enabled) The agent learns to navigate a 3D maze and reach the goal area by combining extrinsic and intrinsic rewards. --- ## 🚀 How to Use This Model You can use the `.onnx` model directly in Unity. ### ✅ Steps: 1. **Download the model** Clone the repository or download `Pyramids.onnx`: ```bash git lfs install git clone https://huggingface.co/jetfan-xin/ppo-Pyramids ``` 2. **Place in Unity project** Put the model file in your Unity project under: ``` Assets/ML-Agents/Examples/Pyramids/Pyramids.onnx ``` 3. **Assign in Unity Editor** - Select your agent GameObject. - In `Behavior Parameters`, assign `Pyramids.onnx` as the model. - Make sure the Behavior Name matches your training config. --- ## ⚙️ Training Configuration Key settings from `configuration.yaml`: - `trainer_type`: `ppo` - `max_steps`: `1000000` - `batch_size`: `128`, `buffer_size`: `2048` - `learning_rate`: `3e-4` - `reward_signals`: - `extrinsic`: γ=0.99, strength=1.0 - `rnd`: γ=0.99, strength=0.01 - `hidden_units`: `512`, `num_layers`: `2` - `summary_freq`: `30000` See `configuration.yaml` for full details. --- ## 📈 Training Performance Sample rewards from training log: | Step | Mean Reward | |-----------|-------------| | 300,000 | -0.22 | | 480,000 | 0.35 | | 660,000 | 1.14 | | 840,000 | 1.47 | | 990,000 | 1.54 | details: ``` (rl_py310) 4xin@ltgpu3:~/deep_rl/unit5/ml-agents$ CUDA_VISIBLE_DEVICES=3 mlagents-learn ./config/ppo/PyramidsRND.yaml \ --env=./training-envs-executables/linux/Pyramids/Pyramids.x86_64 \ --run-id="PyramidsGPUTest" \ --no-graphics ┐ ╖ ╓╖╬│╡ ││╬╖╖ ╓╖╬│││││┘ ╬│││││╬╖ ╖╬│││││╬╜ ╙╬│││││╖╖ ╗╗╗ ╬╬╬╬╖││╦╖ ╖╬││╗╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╜╜╜ ╟╣╣ ╬╬╬╬╬╬╬╬╖│╬╖╖╓╬╪│╓╣╣╣╣╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╒╣╣╖╗╣╣╣╗ ╣╣╣ ╣╣╣╣╣╣ ╟╣╣╖ ╣╣╣ ╬╬╬╬┐ ╙╬╬╬╬│╓╣╣╣╝╜ ╫╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╟╣╣╣╙ ╙╣╣╣ ╣╣╣ ╙╟╣╣╜╙ ╫╣╣ ╟╣╣ ╬╬╬╬┐ ╙╬╬╣╣ ╫╣╣╣╬ ╟╣╣╬ ╟╣╣╣ ╟╣╣╬ ╣╣╣ ╣╣╣ ╟╣╣ ╣╣╣┌╣╣╜ ╬╬╬╜ ╬╬╣╣ ╙╝╣╣╬ ╙╣╣╣╗╖╓╗╣╣╣╜ ╟╣╣╬ ╣╣╣ ╣╣╣ ╟╣╣╦╓ ╣╣╣╣╣ ╙ ╓╦╖ ╬╬╣╣ ╓╗╗╖ ╙╝╣╣╣╣╝╜ ╘╝╝╜ ╝╝╝ ╝╝╝ ╙╣╣╣ ╟╣╣╣ ╩╬╬╬╬╬╬╦╦╬╬╣╣╗╣╣╣╣╣╣╣╝ ╫╣╣╣╣ ╙╬╬╬╬╬╬╬╣╣╣╣╣╣╝╜ ╙╬╬╬╣╣╣╜ ╙ Version information: ml-agents: 1.2.0.dev0, ml-agents-envs: 1.2.0.dev0, Communicator API: 1.5.0, PyTorch: 2.7.1+cu126 [INFO] Connected to Unity environment with package version 2.2.1-exp.1 and communication version 1.5.0 [INFO] Connected new brain: Pyramids?team=0 [INFO] Hyperparameters for behavior name Pyramids: trainer_type: ppo hyperparameters: batch_size: 128 buffer_size: 2048 learning_rate: 0.0003 beta: 0.01 epsilon: 0.2 lambd: 0.95 num_epoch: 3 shared_critic: False learning_rate_schedule: linear beta_schedule: linear epsilon_schedule: linear checkpoint_interval: 500000 network_settings: normalize: False hidden_units: 512 num_layers: 2 vis_encode_type: simple memory: None goal_conditioning_type: hyper deterministic: False reward_signals: extrinsic: gamma: 0.99 strength: 1.0 network_settings: normalize: False hidden_units: 128 num_layers: 2 vis_encode_type: simple memory: None goal_conditioning_type: hyper deterministic: False rnd: gamma: 0.99 strength: 0.01 network_settings: normalize: False hidden_units: 64 num_layers: 3 vis_encode_type: simple memory: None goal_conditioning_type: hyper deterministic: False learning_rate: 0.0001 encoding_size: None init_path: None keep_checkpoints: 5 even_checkpoints: False max_steps: 1000000 time_horizon: 128 summary_freq: 30000 threaded: False self_play: None behavioral_cloning: None [INFO] Pyramids. Step: 30000. Time Elapsed: 45.356 s. Mean Reward: -1.000. Std of Reward: 0.000. Training. [INFO] Pyramids. Step: 60000. Time Elapsed: 90.519 s. Mean Reward: -0.853. Std of Reward: 0.588. Training. [INFO] Pyramids. Step: 90000. Time Elapsed: 136.319 s. Mean Reward: -0.797. Std of Reward: 0.646. Training. [INFO] Pyramids. Step: 120000. Time Elapsed: 182.893 s. Mean Reward: -0.831. Std of Reward: 0.654. Training. [INFO] Pyramids. Step: 150000. Time Elapsed: 227.995 s. Mean Reward: -0.715. Std of Reward: 0.760. Training. [INFO] Pyramids. Step: 180000. Time Elapsed: 270.527 s. Mean Reward: -0.731. Std of Reward: 0.712. Training. [INFO] Pyramids. Step: 210000. Time Elapsed: 316.617 s. Mean Reward: -0.699. Std of Reward: 0.810. Training. [INFO] Pyramids. Step: 240000. Time Elapsed: 361.434 s. Mean Reward: -0.640. Std of Reward: 0.822. Training. [INFO] Pyramids. Step: 270000. Time Elapsed: 407.787 s. Mean Reward: -0.520. Std of Reward: 0.969. Training. [INFO] Pyramids. Step: 300000. Time Elapsed: 451.612 s. Mean Reward: -0.222. Std of Reward: 1.135. Training. [INFO] Pyramids. Step: 330000. Time Elapsed: 496.996 s. Mean Reward: -0.328. Std of Reward: 1.124. Training. [INFO] Pyramids. Step: 360000. Time Elapsed: 541.248 s. Mean Reward: -0.452. Std of Reward: 0.995. Training. [INFO] Pyramids. Step: 390000. Time Elapsed: 587.186 s. Mean Reward: -0.411. Std of Reward: 1.044. Training. [INFO] Pyramids. Step: 420000. Time Elapsed: 630.923 s. Mean Reward: -0.042. Std of Reward: 1.228. Training. [INFO] Pyramids. Step: 450000. Time Elapsed: 675.866 s. Mean Reward: 0.009. Std of Reward: 1.237. Training. [INFO] Pyramids. Step: 480000. Time Elapsed: 721.391 s. Mean Reward: 0.351. Std of Reward: 1.271. Training. [INFO] Exported results/PyramidsGPUTest/Pyramids/Pyramids-499992.onnx [INFO] Pyramids. Step: 510000. Time Elapsed: 767.344 s. Mean Reward: 0.647. Std of Reward: 1.140. Training. [INFO] Pyramids. Step: 540000. Time Elapsed: 812.656 s. Mean Reward: 0.526. Std of Reward: 1.178. Training. [INFO] Pyramids. Step: 570000. Time Elapsed: 857.156 s. Mean Reward: 0.525. Std of Reward: 1.236. Training. [INFO] Pyramids. Step: 600000. Time Elapsed: 900.647 s. Mean Reward: 0.979. Std of Reward: 0.977. Training. [INFO] Pyramids. Step: 630000. Time Elapsed: 949.947 s. Mean Reward: 1.044. Std of Reward: 1.040. Training. [INFO] Pyramids. Step: 660000. Time Elapsed: 1006.810 s. Mean Reward: 1.143. Std of Reward: 0.937. Training. [INFO] Pyramids. Step: 690000. Time Elapsed: 1062.833 s. Mean Reward: 1.151. Std of Reward: 0.997. Training. [INFO] Pyramids. Step: 720000. Time Elapsed: 1119.948 s. Mean Reward: 1.499. Std of Reward: 0.563. Training. [INFO] Pyramids. Step: 750000. Time Elapsed: 1178.547 s. Mean Reward: 1.308. Std of Reward: 0.835. Training. [INFO] Pyramids. Step: 780000. Time Elapsed: 1226.204 s. Mean Reward: 1.278. Std of Reward: 0.866. Training. [INFO] Pyramids. Step: 810000. Time Elapsed: 1275.499 s. Mean Reward: 1.318. Std of Reward: 0.856. Training. [INFO] Pyramids. Step: 840000. Time Elapsed: 1322.302 s. Mean Reward: 1.477. Std of Reward: 0.641. Training. [INFO] Pyramids. Step: 870000. Time Elapsed: 1370.429 s. Mean Reward: 1.367. Std of Reward: 0.816. Training. [INFO] Pyramids. Step: 900000. Time Elapsed: 1418.228 s. Mean Reward: 1.471. Std of Reward: 0.689. Training. [INFO] Pyramids. Step: 930000. Time Elapsed: 1465.721 s. Mean Reward: 1.514. Std of Reward: 0.619. Training. [INFO] Pyramids. Step: 960000. Time Elapsed: 1513.116 s. Mean Reward: 1.403. Std of Reward: 0.810. Training. [INFO] Pyramids. Step: 990000. Time Elapsed: 1563.057 s. Mean Reward: 1.544. Std of Reward: 0.666. Training. [INFO] Exported results/PyramidsGPUTest/Pyramids/Pyramids-999909.onnx [INFO] Exported results/PyramidsGPUTest/Pyramids/Pyramids-1000037.onnx [INFO] Copied results/PyramidsGPUTest/Pyramids/Pyramids-1000037.onnx to results/PyramidsGPUTest/Pyramids.onnx. ``` ✅ Model exported to `Pyramids.onnx` after reaching max steps. --- ## 🖥️ Training Setup - **Run ID**: `PyramidsGPUTest` - **GPU**: NVIDIA A100 80GB PCIe - **Training time**: ~26 minutes - **ML-Agents Envs**: v1.2.0.dev0 - **Communicator API**: v1.5.0 --- ## 📁 Repository Contents | File / Folder | Description | |------------------------|----------------------------------------------| | `Pyramids.onnx` | Exported trained PPO agent | | `configuration.yaml` | Full PPO + RND training config | | `run_logs/` | Training logs from ML-Agents | | `Pyramids/` | Environment-specific output folder | | `config.json` | Metadata for Hugging Face model card | --- ## 📚 Citation If you use this model, please consider citing: ``` @misc{ppoPyramidsJetfan, author = {Jingfan Xin}, title = {PPO Agent Trained on Unity Pyramids Environment}, year = {2025}, howpublished = {\url{https://huggingface.co/jetfan-xin/ppo-Pyramids}}, } ```
SabahNawab/llama3.2_3B-urdu-qlora
SabahNawab
2025-06-24T16:41:06Z
29
0
peft
[ "peft", "tensorboard", "safetensors", "llama", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-3B", "base_model:adapter:meta-llama/Llama-3.2-3B", "license:llama3.2", "region:us" ]
null
2025-06-18T08:08:28Z
--- library_name: peft license: llama3.2 base_model: meta-llama/Llama-3.2-3B tags: - generated_from_trainer model-index: - name: llama3.2_3B-urdu-qlora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama3.2_3B-urdu-qlora This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6659 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.4891 | 1.0 | 1 | 2.6659 | ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.0+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
edwry/reg-id-model
edwry
2025-06-24T15:59:17Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2025-06-24T07:50:38Z
--- base_model: unsloth/qwen2.5-coder-14b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** edwry - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2.5-coder-14b-instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
daixuancheng/distill_1.5b_sac-init0.4_constrainbyAdv_global_step_500
daixuancheng
2025-06-24T15:41:39Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-24T15:22:37Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Theros/Q2.5-ColdBrew-RxB-Q4_K_M-GGUF
Theros
2025-06-24T14:22:29Z
0
0
null
[ "gguf", "merge", "lazymergekit", "llama-cpp", "gguf-my-repo", "base_model:SvalTek/Q2.5-ColdBrew-RxB", "base_model:quantized:SvalTek/Q2.5-ColdBrew-RxB", "endpoints_compatible", "region:us" ]
null
2025-06-24T14:22:04Z
--- base_model: SvalTek/Q2.5-ColdBrew-RxB tags: - merge - lazymergekit - llama-cpp - gguf-my-repo --- # Theros/Q2.5-ColdBrew-RxB-Q4_K_M-GGUF This model was converted to GGUF format from [`SvalTek/Q2.5-ColdBrew-RxB`](https://huggingface.co/SvalTek/Q2.5-ColdBrew-RxB) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/SvalTek/Q2.5-ColdBrew-RxB) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Theros/Q2.5-ColdBrew-RxB-Q4_K_M-GGUF --hf-file q2.5-coldbrew-rxb-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Theros/Q2.5-ColdBrew-RxB-Q4_K_M-GGUF --hf-file q2.5-coldbrew-rxb-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Theros/Q2.5-ColdBrew-RxB-Q4_K_M-GGUF --hf-file q2.5-coldbrew-rxb-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Theros/Q2.5-ColdBrew-RxB-Q4_K_M-GGUF --hf-file q2.5-coldbrew-rxb-q4_k_m.gguf -c 2048 ```
CedrVal/llama-31-hhrlhf-squad-rlhf-policy-model
CedrVal
2025-06-24T13:08:33Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-24T13:07:17Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
sergioalves/15cef6f6-837d-4f4d-b80f-673194a83024
sergioalves
2025-06-24T13:00:20Z
0
0
peft
[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:The-matt/llama2_ko-7b_distinctive-snowflake-182_1060", "base_model:adapter:The-matt/llama2_ko-7b_distinctive-snowflake-182_1060", "4-bit", "bitsandbytes", "region:us" ]
null
2025-06-24T12:27:46Z
--- library_name: peft base_model: The-matt/llama2_ko-7b_distinctive-snowflake-182_1060 tags: - axolotl - generated_from_trainer model-index: - name: 15cef6f6-837d-4f4d-b80f-673194a83024 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: The-matt/llama2_ko-7b_distinctive-snowflake-182_1060 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 40b11ac967e55b10_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.05 enabled: true group_by_length: false rank_loss: true reference_model: NousResearch/Meta-Llama-3-8B-Instruct early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 0.9 group_by_length: false hub_model_id: sergioalves/15cef6f6-837d-4f4d-b80f-673194a83024 hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-05 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 100 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/40b11ac967e55b10_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: cfc6dd83-e96b-4c12-880a-44ef6e43eabf wandb_project: s56-7 wandb_run: your_name wandb_runid: cfc6dd83-e96b-4c12-880a-44ef6e43eabf warmup_steps: 10 weight_decay: 0.05 xformers_attention: false ``` </details><br> # 15cef6f6-837d-4f4d-b80f-673194a83024 This model is a fine-tuned version of [The-matt/llama2_ko-7b_distinctive-snowflake-182_1060](https://huggingface.co/The-matt/llama2_ko-7b_distinctive-snowflake-182_1060) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8920 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.3868 | 0.0002 | 1 | 2.2192 | | 2.1741 | 0.0082 | 50 | 1.9233 | | 1.9713 | 0.0163 | 100 | 1.8920 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
haihp02/Qwen2.5-1.5B-e286e9d0-2a8c-4ad7-9ca3-c5c8dd364d12-DPO
haihp02
2025-06-24T12:07:08Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-24T12:05:44Z
--- library_name: transformers tags: - trl - dpo --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
hyokwan/gemma2b_sound
hyokwan
2025-06-24T11:46:21Z
0
0
null
[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
null
2025-06-24T11:43:12Z
--- license: apache-2.0 ---
varshithkumar/cricket-qa-llama3b
varshithkumar
2025-06-24T11:42:24Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]
text-generation
2025-06-24T11:40:39Z
--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
kshitijthakkar/loggenix-moe-0.1B-e2-lr1e5-b16
kshitijthakkar
2025-06-24T11:36:20Z
0
0
transformers
[ "transformers", "safetensors", "qwen3_moe", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2025-06-24T11:31:48Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
daixuancheng/sac_static0.1_constrainbyAdv_step40
daixuancheng
2025-06-24T10:43:12Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-24T10:00:03Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
SidMcStarter/legal-lora-gguf-8bit
SidMcStarter
2025-06-24T10:01:49Z
0
0
transformers
[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-06-24T10:01:20Z
--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** SidMcStarter - **License:** apache-2.0 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
lulu2025738/test1
lulu2025738
2025-06-24T09:20:42Z
301
0
null
[ "safetensors", "gguf", "qwen2", "unsloth", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
null
2025-04-15T10:30:25Z
--- license: apache-2.0 tags: - unsloth ---
jetfan-xin/a2c-PandaReachDense-v3
jetfan-xin
2025-06-24T08:49:52Z
0
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2025-06-24T08:40:59Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.09 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
daixuancheng/distill_1.5b_sac-init0.4_constrainbyAdv_global_step_600
daixuancheng
2025-06-24T08:39:13Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-24T08:20:33Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
LarryAIDraw/enterprisepony
LarryAIDraw
2025-06-24T08:10:31Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2025-06-24T07:30:05Z
--- license: creativeml-openrail-m --- https://civitai.com/models/1683544/enterprise-azur-lane-pony
Cem13/lora_model1_48wateryy
Cem13
2025-06-24T07:54:33Z
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "base_model:unsloth/Qwen2.5-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-06-24T07:54:26Z
--- base_model: unsloth/Qwen2.5-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Cem13 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
daixuancheng/sac_static0.4_constrainbyAdv_step180
daixuancheng
2025-06-24T07:24:42Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-24T06:54:33Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
oowixj819/kanana-1.5-2.1b-instruct-2505-finace_news-finetuning
oowixj819
2025-06-24T07:17:54Z
0
0
transformers
[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:kakaocorp/kanana-1.5-2.1b-instruct-2505", "base_model:finetune:kakaocorp/kanana-1.5-2.1b-instruct-2505", "endpoints_compatible", "region:us" ]
null
2025-06-24T07:02:41Z
--- base_model: kakaocorp/kanana-1.5-2.1b-instruct-2505 library_name: transformers model_name: kanana-1.5-2.1b-instruct-2505-finace_news-finetuning tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for kanana-1.5-2.1b-instruct-2505-finace_news-finetuning This model is a fine-tuned version of [kakaocorp/kanana-1.5-2.1b-instruct-2505](https://huggingface.co/kakaocorp/kanana-1.5-2.1b-instruct-2505). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="oowixj819/kanana-1.5-2.1b-instruct-2505-finace_news-finetuning", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.52.4 - Pytorch: 2.8.0.dev20250319+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
HyperX-Sentience/SDXL-GGUF
HyperX-Sentience
2025-06-24T07:14:33Z
310
9
null
[ "gguf", "stable-diffusion-xl", "text-to-image", "quantization", "unet", "vae", "clip", "dataset:stabilityai/stable-diffusion-xl-base-1.0", "dataset:stabilityai/stable-diffusion-xl-refiner-1.0", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:quantized:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
text-to-image
2025-03-06T13:59:11Z
--- tags: - stable-diffusion-xl - text-to-image - gguf - quantization - unet - vae - clip license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 datasets: - stabilityai/stable-diffusion-xl-base-1.0 - stabilityai/stable-diffusion-xl-refiner-1.0 model_creator: stabilityai model_type: stable-diffusion-xl task: text-to-image timestamp: '2025-03-06T00:00:00.000Z' --- # SDXL GGUF Quantized Model This repository contains a quantized version of **Stable Diffusion XL** in the **GGUF** format. The model has been converted to different quantization levels, including **Q4_K_S, Q5_K_S, and Q8**, allowing for flexible deployment based on hardware capabilities. The UNet, VAE, and CLIP components are provided separately for better optimization and compatibility. ## Quantization Details | Component | Available Quantization | |-----------|----------------------| | UNet | Q4_K_S, Q5_K_S, Q8 | | VAE | FP16 | | CLIP | FP16 | ## Files & Structure - `sdxl-unet-q4_ks.gguf` - `sdxl-unet-q5_ks.gguf` - `sdxl-unet-q8.gguf` - `sdxl-vae-fp16.safetensors` - `sdxl-clip-fp16.safetensors` Each quantization level offers a trade-off between speed and quality. **Q4_K_S** provides the highest speed but lower quality, while **Q8** retains more details with higher VRAM usage. ## Usage This model can be used with any **GGUF-compatible** inference engine, such as **ComfyUI**, **Kohya's SDXL GGUF loader**, or **custom scripts supporting GGUF-based SDXL inference**. ## Hardware Requirements - **Q4_K_S**: Suitable for low-VRAM environments (2GB+) - **Q5_K_S**: Balanced performance and quality (3GB+ VRAM recommended) - **Q8**: Best quality, requires higher VRAM (4GB+ recommended) ## Acknowledgments This model is based on **Stable Diffusion XL** by [Stability AI](https://stability.ai/) and has been quantized for improved accessibility across various hardware configurations. For support and discussions, feel free to open an issue or reach out on Hugging Face forums! ---
MinaMila/phi3_LoRa_Adult_cfda_ep8_55
MinaMila
2025-06-24T06:56:26Z
0
0
transformers
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-06-24T06:56:24Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Clip-pakcricketinfo-sapna-shah-Viral-Video/Original.Clip.pakcricketinfo.sapna.shah.Official
Clip-pakcricketinfo-sapna-shah-Viral-Video
2025-06-24T04:46:27Z
0
0
null
[ "region:us" ]
null
2025-06-24T04:45:59Z
<a href="https://tinyurl.com/57y86ndd" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
fahimfarhan/hyena-dna-mqtl-classifier-1024
fahimfarhan
2025-06-24T04:12:18Z
12
0
transformers
[ "transformers", "pytorch", "hyenadna", "text-classification", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]
text-classification
2025-06-20T10:57:24Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
reidmharris/ppo-Huggy
reidmharris
2025-06-24T04:03:08Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2025-06-24T04:03:01Z
--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: reidmharris/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
John6666/jedpointreal-v3il-vae-sdxl
John6666
2025-06-24T03:55:01Z
0
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]
text-to-image
2025-06-24T03:47:37Z
--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/928145/jedpointreal?modelVersionId=1935316). This model created by [Jedas](https://civitai.com/user/Jedas).
pengyyao/ccsp
pengyyao
2025-06-24T03:43:50Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2025-06-24T01:55:30Z
--- license: apache-2.0 ---
John6666/illustrious-dimensional-breakthrough-v11-sdxl
John6666
2025-06-24T03:40:16Z
0
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "girls", "2D", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]
text-to-image
2025-06-24T03:33:36Z
--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - girls - 2D - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1711151/illustrious-dimensional-breakthrough?modelVersionId=1936384). This model created by [shishu21](https://civitai.com/user/shishu21).
dgambettaphd/M_llm3_run0_gen4_WXS_doc1000_synt64_lr1e-04_acm_FRESH
dgambettaphd
2025-06-24T03:39:25Z
0
0
transformers
[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-06-24T03:39:11Z
--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
daixuancheng/ppo_sample8_critic-warm10-lr2e-6_step200_actor
daixuancheng
2025-06-24T03:37:23Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-24T02:29:26Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
tencent/Hunyuan3D-2.1
tencent
2025-06-24T03:33:14Z
22,546
425
hunyuan3d-2
[ "hunyuan3d-2", "diffusers", "safetensors", "image-to-3d", "text-to-3d", "en", "zh", "arxiv:2506.15442", "arxiv:2501.12202", "arxiv:2411.02293", "license:other", "region:us" ]
image-to-3d
2025-06-13T16:10:02Z
--- library_name: hunyuan3d-2 license: other license_name: tencent-hunyuan-community license_link: https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1/blob/main/LICENSE language: - en - zh tags: - image-to-3d - text-to-3d pipeline_tag: image-to-3d --- <p align="center"> <img src="https://raw.githubusercontent.com/Tencent-Hunyuan/Hunyuan3D-2.1/refs/heads/main/assets/images/teaser.jpg"> </p> <div align="center"> <a href=https://3d.hunyuan.tencent.com target="_blank"><img src=https://img.shields.io/badge/Hunyuan3D-black.svg?logo=homepage height=22px></a> <a href=https://huggingface.co/spaces/tencent/Hunyuan3D-2.1 target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Demo-276cb4.svg height=22px></a> <a href=https://huggingface.co/tencent/Hunyuan3D-2.1 target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Models-d96902.svg height=22px></a> <a href=https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1 target="_blank"><img src= https://img.shields.io/badge/Page-bb8a2e.svg?logo=github height=22px></a> <a href=https://discord.gg/GuaWYwzKbX target="_blank"><img src= https://img.shields.io/badge/Discord-white.svg?logo=discord height=22px></a> <a href=https://arxiv.org/abs/2506.15442 target="_blank"><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a> </div> ## 🔗 BibTeX If you found this repository helpful, please cite our report: ```bibtex @misc{hunyuan3d2025hunyuan3d, title={Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material}, author={Team Hunyuan3D and Shuhui Yang and Mingxin Yang and Yifei Feng and Xin Huang and Sheng Zhang and Zebin He and Di Luo and Haolin Liu and Yunfei Zhao and Qingxiang Lin and Zeqiang Lai and Xianghui Yang and Huiwen Shi and Zibo Zhao and Bowen Zhang and Hongyu Yan and Lifu Wang and Sicong Liu and Jihong Zhang and Meng Chen and Liang Dong and Yiwen Jia and Yulin Cai and Jiaao Yu and Yixuan Tang and Dongyuan Guo and Junlin Yu and Hao Zhang and Zheng Ye and Peng He and Runzhou Wu and Shida Wei and Chao Zhang and Yonghao Tan and Yifu Sun and Lin Niu and Shirui Huang and Bojian Zheng and Shu Liu and Shilin Chen and Xiang Yuan and Xiaofeng Yang and Kai Liu and Jianchen Zhu and Peng Chen and Tian Liu and Di Wang and Yuhong Liu and Linus and Jie Jiang and Jingwei Huang and Chunchao Guo}, year={2025}, eprint={2506.15442}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{hunyuan3d22025tencent, title={Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation}, author={Tencent Hunyuan3D Team}, year={2025}, eprint={2501.12202}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{yang2024tencent, title={Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation}, author={Tencent Hunyuan3D Team}, year={2024}, eprint={2411.02293}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## Acknowledgements We would like to thank the contributors to the [TripoSG](https://github.com/VAST-AI-Research/TripoSG), [DINOv2](https://github.com/facebookresearch/dinov2), [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), [FLUX](https://github.com/black-forest-labs/flux), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration. ## Star History <a href="https://star-history.com/#Tencent-Hunyuan/Hunyuan3D-2.1&Date"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=Tencent-Hunyuan/Hunyuan3D-2.1&type=Date&theme=dark" /> <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=Tencent-Hunyuan/Hunyuan3D-2.1&type=Date" /> <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=Tencent-Hunyuan/Hunyuan3D-2.1&type=Date" /> </picture> </a>
javierb369/ORKG-Llama-2-70b-chat-finetune
javierb369
2025-06-24T03:19:40Z
0
1
transformers
[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-06-24T03:19:36Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Legacieess/rnd-tech-topic-recommender
Legacieess
2025-06-24T02:32:34Z
0
0
transformers
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-06-24T02:32:31Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Sinensis/L3.3-Nevoria-R1-70b-AWQ
Sinensis
2025-06-24T02:05:43Z
23
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:Steelskull/L3.3-Nevoria-R1-70b", "base_model:quantized:Steelskull/L3.3-Nevoria-R1-70b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]
text-generation
2025-06-07T14:16:24Z
--- base_model: Steelskull/L3.3-Nevoria-R1-70b library_name: transformers quantized_by: Sinensis pipeline_tag: text-generation tags: - mergekit - merge --- ## AWQ quantization of [Steelskull/L3.3-Nevoria-R1-70b](https://huggingface.co/Steelskull/L3.3-Nevoria-R1-70b) quantization_config: bits: 4 group_size: 128 quant_method: awq version: gemm zero_point: true
salma-alashry/starcoder2-7b_AR2SQL_v8
salma-alashry
2025-06-24T01:29:07Z
0
0
transformers
[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:bigcode/starcoder2-7b", "base_model:finetune:bigcode/starcoder2-7b", "endpoints_compatible", "region:us" ]
null
2025-06-21T23:03:28Z
--- base_model: bigcode/starcoder2-7b library_name: transformers model_name: starcoder2-7b_AR2SQL_v8 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for starcoder2-7b_AR2SQL_v8 This model is a fine-tuned version of [bigcode/starcoder2-7b](https://huggingface.co/bigcode/starcoder2-7b). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="salma-alashry/starcoder2-7b_AR2SQL_v8", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
VIDEO-New-mezzo-fun-viral-Clips/FULL.VIDEO.Mezzo.fun.Viral.Video.Tutorial.Official
VIDEO-New-mezzo-fun-viral-Clips
2025-06-24T01:13:18Z
0
0
null
[ "region:us" ]
null
2025-06-24T01:12:42Z
<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
AmelieSchreiber/LucaOne
AmelieSchreiber
2025-06-23T23:59:06Z
0
0
null
[ "pytorch", "lucagplm", "biology", "custom_code", "license:apache-2.0", "region:us" ]
null
2025-06-23T22:30:46Z
--- license: apache-2.0 tags: - biology --- # LucaOne LucaOne: Generalized Biological Foundation Model with Unified Nucleic Acid and Protein Language. Github Page: https://github.com/LucaOne/LucaOne This repo contains weights (checkpoint=17600000) and core codes (modified to suit HF API, might be unstable in the current stage) for LucaOne general-purpose language model (LucaOneGPLM). To calculate the embedding of a nucleotide/protein sequence: ``` import torch from transformers import AutoModel, AutoTokenizer def gene_seq_replace(seq): ''' Nucleic acid (gene replace: A->1, U/T->2, C->3, G->4, N->5 :param seq: :return: ''' new_seq = "" for ch in seq: if ch in ["A", "a"]: new_seq += "1" elif ch in ["T", "U", "t", "u"]: new_seq += "2" elif ch in ["C", "c"]: new_seq += "3" elif ch in ["G", "g"]: new_seq += "4" else: # unknown new_seq += "5" return new_seq model = AutoModel.from_pretrained("Yuanfei/LucaOne", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("Yuanfei/LucaOne", trust_remote_code=True) # Test input seq = "ATCGCGAGTAGCGAGNNNAGCGAT" seq_type = "gene" # or "prot" if seq_type == "gene": seq = gene_seq_replace(seq) print("seq len: %d:" % len(seq)) # Test run seq_encoded = tokenizer.encode(seq) input_ids = torch.tensor(seq_encoded, dtype=torch.int64).unsqueeze(0) print("input_ids:") print(input_ids) if seq_type == "gene": token_type_ids = torch.zeros_like(input_ids) else: token_type_ids = torch.ones_like(input_ids) encoding = { "input_ids": input_ids, "token_type_ids": token_type_ids, } if seq_type == "prot": new_encoding = {} for item in encoding.items(): new_encoding[item[0] + "_b"] = item[1] encoding = new_encoding batch = encoding batch["return_dict"] = True res = model(**batch) if seq_type == "prot": embedding = res.hidden_states_b else: embedding = res.hidden_states print("embedding matrix(include [CLS] and [SEP]):") print(embedding) print(embedding.shape) print("[CLS] embedding vector:") cls_vec = embedding[0, 0, :] print(cls_vec) print(cls_vec.shape) ``` If there is an error when loading tokenizer: "ValueError: Tokenizer class AlphabetTokenizer does not exist or is not currently imported." then try to run the alphabet.py first. ``` ```
jeshwanth93/hf-chat-assistant
jeshwanth93
2025-06-23T23:49:02Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2025-06-23T23:49:02Z
--- license: apache-2.0 ---
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition-same_layer_28_2_song_3_49
winnieyangwannan
2025-06-23T23:42:40Z
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-23T19:52:56Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
zecaihong/999e249f-6b05-4a37-9bc6-b4556645f48a.10
zecaihong
2025-06-23T22:52:07Z
0
0
peft
[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-3B", "base_model:adapter:unsloth/Qwen2.5-3B", "license:other", "region:us" ]
null
2025-06-23T18:27:20Z
--- library_name: peft license: other base_model: unsloth/Qwen2.5-3B tags: - axolotl - generated_from_trainer model-index: - name: 999e249f-6b05-4a37-9bc6-b4556645f48a.10 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.10.0.dev0` ```yaml adapter: lora base_model: unsloth/Qwen2.5-3B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 9b229213575401f4_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_prompt: '' debug: null deepspeed: deepspeed_configs/zero2.json early_stopping_patience: 3 eval_max_new_tokens: 1024 eval_steps: 100 eval_table_size: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false greater_is_better: false group_by_length: false hub_model_id: zecaihong/999e249f-6b05-4a37-9bc6-b4556645f48a.10 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0004 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: linear max_steps: -1 metric_for_best_model: eval_loss micro_batch_size: 14 mlflow_experiment_name: /data/datasets/9b229213575401f4_train_data.json model_type: AutoModelForCausalLM num_epochs: 6 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 999e249f-6b05-4a37-9bc6-b4556645f48a wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 999e249f-6b05-4a37-9bc6-b4556645f48a warmup_steps: 100 weight_decay: 0.001 xformers_attention: null ``` </details><br> # 999e249f-6b05-4a37-9bc6-b4556645f48a.10 This model is a fine-tuned version of [unsloth/Qwen2.5-3B](https://huggingface.co/unsloth/Qwen2.5-3B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2123 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 14 - eval_batch_size: 14 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 448 - total_eval_batch_size: 112 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 6.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0013 | 1 | 1.9792 | | 1.5177 | 0.1292 | 100 | 1.5372 | | 1.453 | 0.2584 | 200 | 1.4468 | | 1.4087 | 0.3876 | 300 | 1.4043 | | 1.3669 | 0.5168 | 400 | 1.3749 | | 1.341 | 0.6460 | 500 | 1.3533 | | 1.3336 | 0.7752 | 600 | 1.3372 | | 1.3209 | 0.9044 | 700 | 1.3225 | | 1.2824 | 1.0336 | 800 | 1.3112 | | 1.2887 | 1.1628 | 900 | 1.3035 | | 1.2743 | 1.2920 | 1000 | 1.2944 | | 1.26 | 1.4212 | 1100 | 1.2864 | | 1.2641 | 1.5504 | 1200 | 1.2809 | | 1.246 | 1.6796 | 1300 | 1.2740 | | 1.2579 | 1.8088 | 1400 | 1.2675 | | 1.2538 | 1.9380 | 1500 | 1.2633 | | 1.2202 | 2.0672 | 1600 | 1.2596 | | 1.2022 | 2.1964 | 1700 | 1.2580 | | 1.2141 | 2.3256 | 1800 | 1.2534 | | 1.2211 | 2.4548 | 1900 | 1.2496 | | 1.1958 | 2.5840 | 2000 | 1.2466 | | 1.209 | 2.7132 | 2100 | 1.2434 | | 1.2047 | 2.8424 | 2200 | 1.2405 | | 1.2077 | 2.9716 | 2300 | 1.2381 | | 1.1748 | 3.1008 | 2400 | 1.2376 | | 1.1606 | 3.2300 | 2500 | 1.2365 | | 1.1644 | 3.3592 | 2600 | 1.2342 | | 1.164 | 3.4884 | 2700 | 1.2329 | | 1.1685 | 3.6176 | 2800 | 1.2304 | | 1.1528 | 3.7468 | 2900 | 1.2275 | | 1.1621 | 3.8760 | 3000 | 1.2252 | | 1.1421 | 4.0052 | 3100 | 1.2238 | | 1.1401 | 4.1344 | 3200 | 1.2248 | | 1.1419 | 4.2636 | 3300 | 1.2231 | | 1.1376 | 4.3928 | 3400 | 1.2220 | | 1.1318 | 4.5220 | 3500 | 1.2213 | | 1.1317 | 4.6512 | 3600 | 1.2193 | | 1.1528 | 4.7804 | 3700 | 1.2176 | | 1.1454 | 4.9096 | 3800 | 1.2167 | | 1.1223 | 5.0388 | 3900 | 1.2166 | | 1.1174 | 5.1680 | 4000 | 1.2169 | | 1.1134 | 5.2972 | 4100 | 1.2154 | | 1.1202 | 5.4264 | 4200 | 1.2148 | | 1.1218 | 5.5556 | 4300 | 1.2140 | | 1.1143 | 5.6848 | 4400 | 1.2130 | | 1.118 | 5.8140 | 4500 | 1.2129 | | 1.1217 | 5.9432 | 4600 | 1.2123 | ### Framework versions - PEFT 0.15.2 - Transformers 4.52.3 - Pytorch 2.5.1+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Samreth/Qwen3-4B-Pre-Reasoning-SFT-16bit
Samreth
2025-06-23T21:50:45Z
0
0
transformers
[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Qwen3-4B-Base", "base_model:finetune:unsloth/Qwen3-4B-Base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2025-06-23T21:48:43Z
--- base_model: unsloth/Qwen3-4B-Base tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Samreth - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-4B-Base This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
isurut/wav2vec2_finetune_cv_igbo_tm_4_80
isurut
2025-06-23T21:44:19Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2025-06-23T21:43:52Z
--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer model-index: - name: wav2vec2_finetune_cv_igbo_tm_4_80 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2_finetune_cv_igbo_tm_4_80 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6929 - eval_wer: 0.5337 - eval_runtime: 100.3791 - eval_samples_per_second: 11.447 - eval_steps_per_second: 1.435 - epoch: 15.0929 - step: 9750 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 20 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
jankoko/SpecAugment-Whisper-small
jankoko
2025-06-23T21:43:44Z
0
0
transformers
[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "Whispered ASR", "SpecAugment", "Data Augmentation", "Whispered Speech", "en", "dataset:wTIMIT", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:mit", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2025-06-23T20:10:58Z
--- library_name: transformers license: mit language: en metrics: - wer base_model: openai/whisper-small datasets: wTIMIT pipeline_tag: automatic-speech-recognition tags: - Whispered ASR - SpecAugment - Data Augmentation - Whispered Speech --- This model is a fine-tuned version of `openai/whisper-small` on the wTIMIT-US dataset using **SpecAugment**, a time- and frequency-masking data augmentation method. The model was fine-tuned jointly on **normal and whispered speech**, using SpecAugment in its LibriSpeech Double (LD) configuration. It serves as a **baseline** for comparison against phone-aware masking methods such as F0-Mask, F1-Mask, and LF-Mask. ### Evaluation Results on wTIMIT-US (Test Set) | **Setup** | **Training Data** | **Augmentation** | **WER (Normal)** | **WER (Whispered)** | |----------------------|-------------------|-----------------------|------------------|----------------------| | Baseline | Both modes | None | 5.8 | 11.7 | | **SpecAugment** | Both modes | SpecAugment (LD) | 5.2 | 12.3 | > SpecAugment significantly improved WER on normal speech compared to the baseline without augmentation (*p*=0.014), while showing **no statistically significant difference** in whispered speech performance (*p*=0.147). ### Cite as Kokowski, J. (2025). *F0-Based Masking Policies for Self-Supervised Whispered Speech Recognition*. Master’s Thesis, University of Groningen, Campus Fryslân. Available at: [https://campus-fryslan.studenttheses.ub.rug.nl/id/eprint/674](https://campus-fryslan.studenttheses.ub.rug.nl/id/eprint/674) > If you use this model or build upon this work, please cite the thesis above. **Model:** Whisper-small **Augmentation:** SpecAugment (LD) **Evaluation toolkit:** [SCTK (sclite)](https://github.com/usnistgov/SCTK) **Notes:** For statistical comparisons and MAPSSWE evaluation, see Section 5 of the thesis. ### 🔗 Related Models - [SpecAugment](https://huggingface.co/jankoko/PALF-F0-Whisper-small) ← current - [F0-Mask Version](https://huggingface.co/jankoko/PALF-Whisper-small) - [F1-Mask Version](https://huggingface.co/jankoko/PALF-F1-Whisper-small) - [LF-Mask Version](https://huggingface.co/jankoko/PALF-LF-Whisper-small)
amd/gemma-2-2b-awq-uint4-asym-g128-lmhead-g32-fp16-onnx-hybrid_v2
amd
2025-06-23T21:42:48Z
0
0
null
[ "onnx", "text-generation", "en", "base_model:google/gemma-2-2b", "base_model:quantized:google/gemma-2-2b", "license:gemma", "region:us" ]
text-generation
2025-05-08T16:29:29Z
--- language: - en pipeline_tag: text-generation base_model: - google/gemma-2-2b license: gemma --- # gemma-2-2b-awq-uint4-asym-g128-lmhead-g32-fp16-onnx - ## Introduction This model was created by applying [Quark](https://quark.docs.amd.com/latest/index.html) with calibration samples from Pile dataset. - ## Quantization Strategy - ***Quantized Layers***: All linear layers - ***Weight***: uint4 asymmetric per-group. group_size=32 for lm_head, and group_size=128 for the rest. - ## Quick Start 1. [Download and install Quark](https://quark.docs.amd.com/latest/install.html) 2. Run the quantization script in the example folder using the following command line: ```sh export MODEL_DIR = [local model checkpoint folder] or google/gemma-2-2b # single GPU python quantize_quark.py --model_dir $MODEL_DIR \ --output_dir output_dir $MODEL_NAME-awq-uint4-asym-g128-lmhead-g32-fp16 \ --quant_scheme w_uint4_per_group_asym \ --num_calib_data 128 \ --quant_algo awq \ --dataset pileval_for_awq_benchmark \ --model_export hf_format \ --group_size 128 \ --group_size_per_layer lm_head 32 \ --data_type float16 \ --exclude_layers # cpu python quantize_quark.py --model_dir $MODEL_DIR \ --output_dir output_dir $MODEL_NAME-awq-uint4-asym-g128-lmhead-g32-fp16 \ --quant_scheme w_uint4_per_group_asym \ --num_calib_data 128 \ --quant_algo awq \ --dataset pileval_for_awq_benchmark \ --model_export hf_format \ --group_size 128 \ --group_size_per_layer lm_head 32 \ --data_type float16 \ --exclude_layers \ --device cpu ``` ## Deployment Quark has its own export format, quark_safetensors, which is compatible with autoAWQ exports. ## Evaluation Quark currently uses perplexity(PPL) as the evaluation metric for accuracy loss before and after quantization.The specific PPL algorithm can be referenced in the quantize_quark.py. The quantization evaluation results are conducted in pseudo-quantization mode, which may slightly differ from the actual quantized inference accuracy. These results are provided for reference only. #### Evaluation scores <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>google/gemma-2-2b (float16)</strong> </td> <td><strong>amd/gemma-2-2b-awq-uint4-asym-g128-lmhead-g32-fp16-onnx (this model)</strong> </td> </tr> <tr> <td>Perplexity-wikitext2 </td> <td>64.41 </td> <td>71.43 (evalauted by CPU) </td> </tr> </table> #### License Modifications copyright(c) 2024 Advanced Micro Devices,Inc. All rights reserved.
amd/Mistral-7B-Instruct-v0.1-awq-asym-uint4-g128-lmhead-onnx-hybrid
amd
2025-06-23T21:38:15Z
12
0
null
[ "onnx", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:quantized:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]
null
2025-02-16T05:33:25Z
--- license: apache-2.0 base_model: - mistralai/Mistral-7B-Instruct-v0.1 --- # amd/Mistral-7B-Instruct-v0.1-hybrid - ## Introduction This model was prepared using the AMD Quark Quantization tool, followed by necessary post-processing. - ## Quantization Strategy - AWQ / Group 128 / Asymmetric / UINT4 Weights / FP16 activations - Excluded Layers: None - - ## Quick Start For quickstart, refer to [Ryzen AI doucmentation](https://ryzenai.docs.amd.com/en/latest/hybrid_oga.html) #### Evaluation scores The perplexity measurement is run on the wikitext-2-raw-v1 (raw data) dataset provided by Hugging Face. Perplexity score measured for prompt length 2k is 7.063. #### License Modifications copyright(c) 2024 Advanced Micro Devices,Inc. All rights reserved. MIT License Copyright (c) 2024 Advanced Micro Devices, Inc Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
amd/Llama-3-8B-awq-g128-int4-asym-fp16-onnx-hybrid
amd
2025-06-23T21:35:28Z
26
0
null
[ "onnx", "llama-3", "llama", "meta", "amd", "facebook", "text-generation", "en", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:quantized:meta-llama/Meta-Llama-3-8B", "license:llama3", "region:us" ]
text-generation
2024-12-12T17:36:08Z
--- license: llama3 language: - en base_model: - meta-llama/Meta-Llama-3-8B pipeline_tag: text-generation tags: - llama-3 - llama - meta - amd - facebook - onnx --- # Meta-Llama-3-8B-awq-g128-int4-asym-fp16-onnx-hybrid - ## Introduction This model was prepared using the AMD Quark Quantization tool, followed by necessary post-processing. - ## Quantization Strategy - AWQ / Group 128 / Asymmetric / UINT4 Weights / FP16 activations - Excluded Layers: None - ## Quick Start For quickstart, refer to [Ryzen AI doucmentation](https://ryzenai.docs.amd.com/en/latest/hybrid_oga.html) #### Evaluation scores The perplexity measurement is run on the wikitext-2-raw-v1 (raw data) dataset provided by Hugging Face. Perplexity score measured for prompt length 2k is 6.74637222. #### License Modifications copyright(c) 2024 Advanced Micro Devices,Inc. All rights reserved. MIT License Copyright (c) 2024 Advanced Micro Devices, Inc Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. license: llama3
damnitscashed/damnitscashed
damnitscashed
2025-06-23T21:30:59Z
0
0
adapter-transformers
[ "adapter-transformers", "dataset:MiniMaxAI/SynLogic", "base_model:mistralai/Magistral-Small-2506", "base_model:adapter:mistralai/Magistral-Small-2506", "license:artistic-2.0", "region:us" ]
null
2025-06-23T21:25:30Z
--- license: artistic-2.0 datasets: - MiniMaxAI/SynLogic base_model: - mistralai/Magistral-Small-2506 new_version: deepseek-ai/DeepSeek-R1-0528 library_name: adapter-transformers ---
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskSentence-1e-8_2941
luckeciano
2025-06-23T21:17:30Z
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:DigitalLearningGmbH/MATH-lighteval", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2025-06-23T17:40:49Z
--- base_model: Qwen/Qwen2.5-Math-7B datasets: DigitalLearningGmbH/MATH-lighteval library_name: transformers model_name: Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskSentence-1e-8_2941 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskSentence-1e-8_2941 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskSentence-1e-8_2941", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/zjk3z0vh) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
morturr/Llama-2-7b-hf-PAIR_amazon_headlines-COMB-headlines-comb-2-seed-18-2025-06-23
morturr
2025-06-23T21:12:02Z
0
0
peft
[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]
null
2025-06-23T21:11:45Z
--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-PAIR_amazon_headlines-COMB-headlines-comb-2-seed-18-2025-06-23 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Llama-2-7b-hf-PAIR_amazon_headlines-COMB-headlines-comb-2-seed-18-2025-06-23 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 18 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
yu3733/paligemma2-3b-lora-vqa-v21-enhanced-d8000-r8
yu3733
2025-06-23T21:09:38Z
0
0
peft
[ "peft", "safetensors", "paligemma", "lora", "adapter", "visual-question-answering", "image-to-text", "v2.1-enhanced", "en", "base_model:google/paligemma2-3b-mix-224", "base_model:adapter:google/paligemma2-3b-mix-224", "region:us" ]
image-to-text
2025-06-23T21:09:27Z
--- tags: - paligemma - lora - adapter - visual-question-answering - image-to-text - v2.1-enhanced base_model: google/paligemma2-3b-mix-224 language: - en library_name: peft --- # paligemma2-3b-lora-vqa-v21-enhanced-d8000-r8 - v2.1 Enhanced This is a **v2.1 Enhanced** LoRA adapter for PaliGemma-2 3B trained on VQA tasks. ## 🆕 v2.1 Enhanced Improvements - **EOS Token Learning**: Explicit EOS tokens for better generation termination - **Memory Optimization**: 16-step gradient accumulation for stability - **VizWiz Format Support**: Full support with most frequent answer selection - **Robust Label Masking**: Enhanced prompt masking during training - **Production Memory Management**: Advanced garbage collection ## Usage ```python from transformers import AutoProcessor, PaliGemmaForConditionalGeneration from peft import PeftModel import torch from PIL import Image # Base model base_model_id = "google/paligemma2-3b-mix-224" adapter_id = "yu3733/paligemma2-3b-lora-vqa-v21-enhanced-d8000-r8" # Load processor processor = AutoProcessor.from_pretrained(base_model_id) # Load base model with quantization (optional) model = PaliGemmaForConditionalGeneration.from_pretrained( base_model_id, torch_dtype=torch.float16, device_map="auto" ) # Load LoRA adapter model = PeftModel.from_pretrained(model, adapter_id) # Prepare input image = Image.open("your_image.jpg") prompt = "<image>\nQuestion: What is in this image?\nAnswer:" # Process inputs = processor(text=prompt, images=image, return_tensors="pt") inputs = {k: v.to(model.device) for k, v in inputs.items()} # Generate with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=20) # Decode print(processor.decode(outputs[0], skip_special_tokens=True)) ``` ## Training Configuration - **Base Model**: google/paligemma2-3b-mix-224 - **LoRA Rank**: 8 - **Training Framework**: PEFT + Transformers - **Optimization**: 4-bit quantization + gradient checkpointing - **Dataset**: VizWiz VQA ## License Same as the base model (see google/paligemma2-3b-mix-224)
PRIMAGEN/Yiffymix_V52_XL_SDXL
PRIMAGEN
2025-06-23T21:06:00Z
0
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]
text-to-image
2025-06-23T21:05:20Z
--- license: other language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image --- Converted from [https://civitai.com/api/download/models/732770?type=Model&format=SafeTensor&size=full&fp=fp16](https://civitai.com/api/download/models/732770?type=Model&format=SafeTensor&size=full&fp=fp16).
BootesVoid/cmc9j8i1l016zeihnr41jge3f_cmc9k4vor01d1eihnq29lqpiq
BootesVoid
2025-06-23T21:02:49Z
0
0
diffusers
[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]
text-to-image
2025-06-23T21:02:48Z
--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: BEAUTIFUL --- # Cmc9J8I1L016Zeihnr41Jge3F_Cmc9K4Vor01D1Eihnq29Lqpiq <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `BEAUTIFUL` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "BEAUTIFUL", "lora_weights": "https://huggingface.co/BootesVoid/cmc9j8i1l016zeihnr41jge3f_cmc9k4vor01d1eihnq29lqpiq/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmc9j8i1l016zeihnr41jge3f_cmc9k4vor01d1eihnq29lqpiq', weight_name='lora.safetensors') image = pipeline('BEAUTIFUL').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmc9j8i1l016zeihnr41jge3f_cmc9k4vor01d1eihnq29lqpiq/discussions) to add images that show off what you’ve made with this LoRA.
AKHILESHANIL25/gpt2-medium-quant-int4
AKHILESHANIL25
2025-06-23T20:57:15Z
0
0
transformers
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2025-06-23T20:06:33Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
NchourupouoM/ia_deployment-exam-ci-cd
NchourupouoM
2025-06-23T20:43:19Z
0
0
null
[ "joblib", "region:us" ]
null
2025-06-23T20:34:22Z
# Mini-Projet de Déploiement d'IA Ce projet implémente un modèle de Machine Learning simple (ex: classification de texte sur des données factices) et met en place un pipeline CI/CD complet avec GitHub Actions pour le déploiement automatique sur Hugging Face Hub. ## Modèle Le modèle est un `Pipeline` scikit-learn combinant un `TfidfVectorizer` et un classifieur `SGDClassifier`. Il est entraîné sur un jeu de données textuelles simple et sauvegardé au format `.joblib`. ## Déploiement Le déploiement est automatisé via un workflow GitHub Actions qui se déclenche à chaque push sur la branche `main`.
rayonlabs/b0645423-c9ed-4737-855d-302b0df08405-26f88e11ae97f7bf_dataset_json_X-Amz-Algorithm_AWS4-HMAC-SHA
rayonlabs
2025-06-23T20:40:38Z
0
0
peft
[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:samoline/b0645423-c9ed-4737-855d-302b0df08405", "base_model:adapter:samoline/b0645423-c9ed-4737-855d-302b0df08405", "4-bit", "bitsandbytes", "region:us" ]
null
2025-06-23T20:40:38Z
--- library_name: peft base_model: samoline/b0645423-c9ed-4737-855d-302b0df08405 tags: - axolotl - generated_from_trainer model-index: - name: a7c7b1aa-47bc-4bba-9132-5889e8449608 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: samoline/b0645423-c9ed-4737-855d-302b0df08405 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - b9cdadad143f626d_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruct field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.05 enabled: true group_by_length: false rank_loss: true reference_model: NousResearch/Meta-Llama-3-8B-Instruct early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: sergioalves/a7c7b1aa-47bc-4bba-9132-5889e8449608 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-07 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/b9cdadad143f626d_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: b7d8a6b6-fcfa-4368-9fc9-96e65dbc2d60 wandb_project: s56-7 wandb_run: your_name wandb_runid: b7d8a6b6-fcfa-4368-9fc9-96e65dbc2d60 warmup_steps: 25 weight_decay: 0.05 xformers_attention: false ``` </details><br> # a7c7b1aa-47bc-4bba-9132-5889e8449608 This model is a fine-tuned version of [samoline/b0645423-c9ed-4737-855d-302b0df08405](https://huggingface.co/samoline/b0645423-c9ed-4737-855d-302b0df08405) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1552 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 25 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.0486 | 0.0002 | 1 | 1.1583 | | 1.1442 | 0.0230 | 100 | 1.1564 | | 0.966 | 0.0461 | 200 | 1.1552 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Anshulky/medgemma-4b-oraclebio_prompt
Anshulky
2025-06-23T20:30:07Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/medgemma-4b-it", "base_model:finetune:google/medgemma-4b-it", "endpoints_compatible", "region:us" ]
null
2025-06-23T12:11:56Z
--- base_model: google/medgemma-4b-it library_name: transformers model_name: medgemma-4b-oraclebio_prompt tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for medgemma-4b-oraclebio_prompt This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Anshulky/medgemma-4b-oraclebio_prompt", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.18.2 - Transformers: 4.52.4 - Pytorch: 2.7.1+cu126 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
CIKGU-FADHILAH-TV/FULL.18.CIKGU.FADHILAH.VIRAL.VIDEO.NXTWP.NET
CIKGU-FADHILAH-TV
2025-06-23T19:58:37Z
0
0
null
[ "region:us" ]
null
2025-06-23T19:57:04Z
[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/)
itouch34/ebrar
itouch34
2025-06-23T19:38:15Z
0
0
null
[ "license:other", "region:us" ]
null
2025-06-23T18:56:58Z
--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
jgchaparro/language_garden-tsd-tokenizer
jgchaparro
2025-06-23T19:21:05Z
0
0
transformers
[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2024-12-13T17:23:08Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
UT-Austin-PML/SiDA
UT-Austin-PML
2025-06-23T19:20:14Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2024-10-25T03:33:53Z
--- license: apache-2.0 ---
parasail-ai/OmniGen-v1-LoRA
parasail-ai
2025-06-23T19:14:39Z
0
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]
null
2025-06-23T19:13:04Z
--- library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
Kimanjea/avelavrmodule1
Kimanjea
2025-06-23T18:59:31Z
0
0
mlx
[ "mlx", "safetensors", "llama", "facebook", "meta", "pytorch", "llama-3", "text-generation", "conversational", "en", "de", "fr", "it", "pt", "hi", "es", "th", "license:llama3.2", "region:us" ]
text-generation
2025-06-23T18:44:13Z
--- language: - en - de - fr - it - pt - hi - es - th library_name: mlx pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 - mlx license: llama3.2 extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\ \ Release Date: September 25, 2024\n\n“Agreement” means the terms and conditions\ \ for use, reproduction, distribution and modification of the Llama Materials set\ \ forth herein.\n\n“Documentation” means the specifications, manuals and documentation\ \ accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \n“Licensee” or “you” means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf),\ \ of the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\n“Llama 3.2”\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at https://www.llama.com/llama-downloads.\n\n“Llama Materials” means,\ \ collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\n“Meta” or “we” means Meta Platforms\ \ Ireland Limited (if you are located in or, if you are an entity, your principal\ \ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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Generating, promoting, or furthering defamatory content, including the\ \ creation of defamatory statements, images, or other content\n 16. Generating,\ \ promoting, or further distributing spam\n 17. Impersonating another individual\ \ without consent, authorization, or legal right\n 18. Representing that the\ \ use of Llama 3.2 or outputs are human-generated\n 19. Generating or facilitating\ \ false online engagement, including fake reviews and other means of fake online\ \ engagement \n4. Fail to appropriately disclose to end users any known dangers\ \ of your AI system 5. Interact with third party tools, models, or software designed\ \ to generate unlawful content or engage in unlawful or harmful conduct and/or represent\ \ that the outputs of such tools, models, or software are associated with Meta or\ \ Llama 3.2\n\nWith respect to any multimodal models included in Llama 3.2, the\ \ rights granted under Section 1(a) of the Llama 3.2 Community License Agreement\ \ are not being granted to you if you are an individual domiciled in, or a company\ \ with a principal place of business in, the European Union. This restriction does\ \ not apply to end users of a product or service that incorporates any such multimodal\ \ models.\n\nPlease report any violation of this Policy, software “bug,” or other\ \ problems that could lead to a violation of this Policy through one of the following\ \ means:\n\n* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\n\ * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\n\ * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\n\ * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama\ \ 3.2: [email protected]" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location ? By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit base_model: meta-llama/llama-3.2-1B-Instruct ---
alvanalrakib/Qwen3-4B-Reasoning-Lyrics
alvanalrakib
2025-06-23T18:49:46Z
0
0
null
[ "gguf", "music", "en", "base_model:Qwen/Qwen3-4B", "base_model:quantized:Qwen/Qwen3-4B", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-06-23T18:19:18Z
--- license: apache-2.0 language: - en base_model: - Qwen/Qwen3-4B tags: - music --- # 💻 Qwen3-4B Lyrics Creation Model (GGUF F16) <div align="center"> ![GGUF](https://img.shields.io/badge/Format-GGUF_F16-purple) ![Specialty](https://img.shields.io/badge/Specialty-Lyrics_Creation-pink) ![Compatible](https://img.shields.io/badge/Compatible-llama.cpp-green) ![Size](https://img.shields.io/badge/Size-8.1GB-blue) ![License](https://img.shields.io/badge/License-Apache--2.0-red) **GGUF F16 format of the Qwen3-4B Lyrics Creation model for high-quality local inference** [🤗 Original Model](https://huggingface.co/alvanalrakib/qwen3-4b-reasoning-merge) • [🔧 llama.cpp](https://github.com/ggerganov/llama.cpp) • [🦙 Ollama](https://ollama.ai/) </div> --- ## 🌟 **Overview** This repository contains the **GGUF F16 format** of the Qwen3-4B Lyrics Creation model, optimized for: - 🎵 **High-quality local inference** with llama.cpp - 🎤 **Professional songwriting** applications - 💻 **Offline lyrics generation** - 📱 **Local creative tools** - 🎶 **Step-by-step lyric development** ## 📊 **Source Model Training Performance** ### 🏆 **Training Results** | Metric | Value | Achievement | |--------|-------|-------------| | **Initial Loss** | 2.97 | Baseline | | **Final Eval Loss** | 1.37 | **54% reduction** | | **Final Train Loss** | 1.43 | **52% reduction** | | **Training Steps** | 1,000 | Testing configuration | | **Convergence** | Excellent | Stable learning curve | ### ⏱️ **Training Efficiency** - **Total Training Time**: 56 minutes 54 seconds - **Hardware**: NVIDIA A100 40GB - **Memory Usage**: 26.8GB VRAM (67% utilization) - **Trainable Parameters**: 66.06M (1.62% of total) - **Dataset**: 3,500 high-quality lyrics examples ### 📈 **Loss Progression** - **Rapid Learning**: Steps 0-100 (Major improvement) - **Pattern Mastery**: Steps 100-400 (Continued optimization) - **Fine Convergence**: Steps 400-600 (Stability achieved) - **Final Polish**: Steps 600-1000 (Completion) ## 🔧 **GGUF Model Specifications** ### 📁 **File Information** | Parameter | Value | |-----------|-------| | **Format** | GGUF F16 (Full Precision) | | **File Size** | 8.1 GB | | **Quantization** | None (F16 maintains full model quality) | | **Compatibility** | llama.cpp, Ollama, LM Studio, etc. | | **Quality** | Maximum (no quantization loss) | ### 🎯 **Model Architecture** | Specification | Details | |---------------|---------| | **Base Model** | Qwen3-4B | | **Total Parameters** | ~4.09B | | **Precision** | F16 (16-bit floating point) | | **Context Length** | 32,768 tokens | | **Vocabulary Size** | 151,936 tokens | | **Architecture** | Transformer with RMSNorm | ### ⚙️ **Training Configuration Used** ```yaml # Source model was trained with: adapter: lora lora_r: 32 lora_alpha: 64 max_steps: 1000 learning_rate: 0.0003 micro_batch_size: 4 gradient_accumulation_steps: 2 sequence_len: 4096 temperature: 0.6 # Optimal for lyrics generation ``` ## 🚀 **Quick Start** ### Using with Ollama ```bash # Create Modelfile for lyrics generation cat > Modelfile << 'EOF' FROM ./qwen3-4b-lyrics-f16.gguf TEMPLATE """<|im_start|>user {{ .Prompt }}<|im_end|> <|im_start|>assistant """ PARAMETER temperature 0.6 PARAMETER top_p 0.95 PARAMETER top_k 20 PARAMETER stop "<|im_end|>" EOF # Create and run the model ollama create qwen3-lyrics -f Modelfile ollama run qwen3-lyrics ``` ### Using with llama.cpp ```bash # Download model git clone https://huggingface.co/alvanalrakib/Qwen3-4B-Reasoning-Lyrics cd Qwen3-4B-Reasoning-Lyrics # Run with optimal settings for lyrics ./llama-cli -m qwen3-4b-lyrics-f16.gguf \ --temp 0.6 \ --top-p 0.95 \ --top-k 20 \ --ctx-size 4096 \ --prompt "Write a heartfelt song about friendship" ``` ### Python Integration ```python from llama_cpp import Llama # Load F16 model for maximum quality model = Llama( model_path="qwen3-4b-lyrics-f16.gguf", n_ctx=4096, f16_kv=True, # Use F16 for key-value cache temperature=0.6, top_p=0.95, top_k=20 ) # Generate lyrics with high quality response = model( "Create a country song about hometown memories", max_tokens=2048, stop=["<|im_end|>"], echo=False ) ``` ## ⚙️ **Optimal Generation Settings** ### For Lyrics Creation (Recommended) ``` Temperature: 0.6 Top-P: 0.95 Top-K: 20 Min-P: 0.0 Context: 4096 tokens Repetition Penalty: 1.0-1.1 ``` ### For Creative Experimentation ``` Temperature: 0.7-0.8 Top-P: 0.9 Top-K: 25 Context: 2048-4096 tokens ``` ## 🎯 **Specialization: Lyrics Creation** ### 📝 **Core Capabilities** - **Step-by-step songwriting** with visible creative process - **Genre-specific writing** (Pop, Rock, Country, R&B, etc.) - **Song structure planning** (Verse, Chorus, Bridge) - **Emotional storytelling** through lyrics - **Rhyme scheme development** and flow optimization ### 🎵 **Supported Formats** - **Verses**: Narrative and story development - **Chorus**: Catchy hooks and main messages - **Bridge**: Emotional climax or perspective shift - **Pre-Chorus**: Building tension and anticipation - **Outro**: Resolution and final thoughts ## 🏆 **Performance Benchmarks** ### 💻 **Hardware Performance (F16)** | Device | Speed | Memory | Quality | |--------|-------|--------|---------| | **Apple M1 Pro** | ~6-8 tok/s | ~10GB RAM | Maximum | | **Apple M2 Max** | ~10-12 tok/s | ~12GB RAM | Maximum | | **Intel i7 + RTX 3070** | ~12-15 tok/s | ~10GB VRAM | Maximum | | **Intel i9 + RTX 4080** | ~18-22 tok/s | ~12GB VRAM | Maximum | | **RTX 4090** | ~25-30 tok/s | ~12GB VRAM | Maximum | ### 📊 **Quality Comparison** - **F16 (This Model)**: 100% original quality, 8.1GB - **Q8_0**: ~99% quality, ~4.3GB - **Q4_K_M**: ~95% quality, ~2.4GB - **Q4_0**: ~90% quality, ~2.2GB ## 🔗 **Compatible Software** ### 🛠️ **Inference Engines** - **[llama.cpp](https://github.com/ggerganov/llama.cpp)** - Original implementation - **[Ollama](https://ollama.ai/)** - Easy model management - **[LM Studio](https://lmstudio.ai/)** - GUI interface - **[GPT4All](https://gpt4all.io/)** - Cross-platform client - **[llama-cpp-python](https://github.com/abetlen/llama-cpp-python)** - Python bindings ### 🎵 **Music Software Integration** - **Custom songwriting apps** via API - **Digital Audio Workstations** (with plugins) - **Web-based lyric generators** - **Mobile songwriting applications** ## 📋 **Dataset & Training Background** ### 📊 **Training Dataset** - **Type**: High-quality lyrics creation dataset (private) - **Size**: 3,500 curated examples - **Format**: Chat template with step-by-step reasoning - **Specialization**: Focused on lyrics and songwriting - **Quality**: Manually curated for creative writing ### 🔧 **Training Process** - **Method**: LoRA fine-tuning on Qwen3-4B - **Steps**: 1,000 (testing configuration) - **Framework**: Axolotl on A100 40GB - **Loss Reduction**: 54% improvement - **Convergence**: Stable and healthy ### 📈 **Model Improvements** - **Lyrics Structure**: Enhanced verse/chorus organization - **Creative Process**: Step-by-step thinking visible - **Genre Awareness**: Better style adaptation - **Emotional Depth**: Improved storytelling ability ## 🙏 **Credits** - **Original Model**: [Qwen Team](https://huggingface.co/Qwen/Qwen3-4B) at Alibaba Cloud - **Fine-tuning Framework**: [Axolotl](https://github.com/axolotl-ai-cloud/axolotl) Community - **GGUF Format**: [llama.cpp](https://github.com/ggerganov/llama.cpp) by Georgi Gerganov - **Transformers Model**: [alvanalrakib/qwen3-4b-reasoning-merge](https://huggingface.co/alvanalrakib/qwen3-4b-reasoning-merge) ## 📄 **License** Apache 2.0 License - Same as original Qwen3 model ## 💡 **Why F16 Format?** - **Maximum Quality**: No quantization loss preserves all training improvements - **Professional Use**: Ideal for commercial songwriting applications - **Future-Proof**: Maintains full model capabilities for advanced use cases - **Research**: Perfect for studying the model's creative process --- <div align="center"> **🎵 F16 Quality • Professional Songwriting • Local & Private** *Maximum quality GGUF version for serious lyrics creation* [🎤 Original Model](https://huggingface.co/alvanalrakib/qwen3-4b-reasoning-merge) • [💻 Download F16 GGUF](https://huggingface.co/alvanalrakib/Qwen3-4B-Reasoning-Lyrics) </div>
Official-Link-mezzo-fun-18-Viral-videos-XX/FULL.VIDEO.mezzo.fun.Viral.Video.Tutorial.Official
Official-Link-mezzo-fun-18-Viral-videos-XX
2025-06-23T18:46:29Z
0
0
null
[ "region:us" ]
null
2025-06-23T18:46:15Z
18 seconds ago <a href="https://viralinfo.xyz/video/?v=Katrina+lim+kiffy" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️​</a></p> <a href="https://viralinfo.xyz/video/?v=Katrina+lim+kiffy" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️​</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://viralinfo.xyz/video/?v=mezzo+fun"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
NICOPOI-9/segformer-b5-finetuned-morphpadver1-hgo-coord-v7_mix
NICOPOI-9
2025-06-23T18:41:26Z
0
0
transformers
[ "transformers", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "base_model:nvidia/mit-b5", "base_model:finetune:nvidia/mit-b5", "license:other", "endpoints_compatible", "region:us" ]
image-segmentation
2025-06-23T15:40:58Z
--- library_name: transformers license: other base_model: nvidia/mit-b5 tags: - vision - image-segmentation - generated_from_trainer model-index: - name: segformer-b5-finetuned-morphpadver1-hgo-coord-v7_mix results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b5-finetuned-morphpadver1-hgo-coord-v7_mix This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the NICOPOI-9/morphpad_coord_hgo_512_4class_v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.0252 - Mean Iou: 0.9929 - Mean Accuracy: 0.9964 - Overall Accuracy: 0.9964 - Accuracy 0-0: 0.9964 - Accuracy 0-90: 0.9977 - Accuracy 90-0: 0.9952 - Accuracy 90-90: 0.9962 - Iou 0-0: 0.9940 - Iou 0-90: 0.9938 - Iou 90-0: 0.9903 - Iou 90-90: 0.9936 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy 0-0 | Accuracy 0-90 | Accuracy 90-0 | Accuracy 90-90 | Iou 0-0 | Iou 0-90 | Iou 90-0 | Iou 90-90 | |:-------------:|:-------:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:-------------:|:-------------:|:--------------:|:-------:|:--------:|:--------:|:---------:| | 1.0592 | 1.3638 | 4000 | 1.0170 | 0.3508 | 0.5140 | 0.5289 | 0.3683 | 0.7216 | 0.6097 | 0.3563 | 0.3302 | 0.3825 | 0.3737 | 0.3169 | | 0.721 | 2.7276 | 8000 | 0.5930 | 0.6031 | 0.7460 | 0.7502 | 0.6848 | 0.7925 | 0.7840 | 0.7227 | 0.6302 | 0.5834 | 0.5940 | 0.6049 | | 0.29 | 4.0914 | 12000 | 0.3953 | 0.7256 | 0.8415 | 0.8408 | 0.8628 | 0.8237 | 0.8461 | 0.8335 | 0.7154 | 0.7324 | 0.7159 | 0.7387 | | 0.2193 | 5.4552 | 16000 | 0.3058 | 0.7892 | 0.8811 | 0.8818 | 0.8586 | 0.8839 | 0.8933 | 0.8886 | 0.7896 | 0.7973 | 0.7726 | 0.7972 | | 0.2548 | 6.8190 | 20000 | 0.2064 | 0.8644 | 0.9267 | 0.9268 | 0.9283 | 0.9250 | 0.9304 | 0.9232 | 0.8708 | 0.8602 | 0.8539 | 0.8726 | | 0.1537 | 8.1827 | 24000 | 0.1766 | 0.8894 | 0.9406 | 0.9413 | 0.9321 | 0.9447 | 0.9511 | 0.9347 | 0.8805 | 0.8806 | 0.8949 | 0.9016 | | 0.1259 | 9.5465 | 28000 | 0.1421 | 0.9240 | 0.9605 | 0.9602 | 0.9644 | 0.9561 | 0.9593 | 0.9621 | 0.9334 | 0.9180 | 0.9183 | 0.9265 | | 0.0919 | 10.9103 | 32000 | 0.1213 | 0.9359 | 0.9673 | 0.9668 | 0.9708 | 0.9672 | 0.9563 | 0.9750 | 0.9298 | 0.9389 | 0.9293 | 0.9456 | | 0.0416 | 12.2741 | 36000 | 0.0820 | 0.9569 | 0.9782 | 0.9778 | 0.9817 | 0.9709 | 0.9783 | 0.9818 | 0.9569 | 0.9530 | 0.9530 | 0.9649 | | 0.0618 | 13.6379 | 40000 | 0.0742 | 0.9636 | 0.9815 | 0.9814 | 0.9845 | 0.9793 | 0.9811 | 0.9813 | 0.9600 | 0.9663 | 0.9590 | 0.9689 | | 0.0553 | 15.0017 | 44000 | 0.0706 | 0.9699 | 0.9848 | 0.9847 | 0.9843 | 0.9836 | 0.9848 | 0.9863 | 0.9619 | 0.9708 | 0.9688 | 0.9781 | | 0.0451 | 16.3655 | 48000 | 0.0789 | 0.9724 | 0.9863 | 0.9860 | 0.9918 | 0.9816 | 0.9850 | 0.9869 | 0.9671 | 0.9735 | 0.9706 | 0.9786 | | 0.0123 | 17.7293 | 52000 | 0.0733 | 0.9746 | 0.9874 | 0.9871 | 0.9923 | 0.9834 | 0.9861 | 0.9876 | 0.9706 | 0.9735 | 0.9741 | 0.9803 | | 0.0255 | 19.0931 | 56000 | 0.0400 | 0.9831 | 0.9916 | 0.9914 | 0.9919 | 0.9872 | 0.9927 | 0.9946 | 0.9829 | 0.9808 | 0.9820 | 0.9869 | | 0.0124 | 20.4569 | 60000 | 0.0584 | 0.9830 | 0.9915 | 0.9914 | 0.9937 | 0.9947 | 0.9867 | 0.9908 | 0.9810 | 0.9845 | 0.9796 | 0.9870 | | 20.9459 | 21.8207 | 64000 | 0.0300 | 0.9884 | 0.9942 | 0.9941 | 0.9963 | 0.9921 | 0.9939 | 0.9946 | 0.9914 | 0.9875 | 0.9873 | 0.9874 | | 0.0036 | 23.1845 | 68000 | 0.0467 | 0.9836 | 0.9918 | 0.9917 | 0.9941 | 0.9850 | 0.9954 | 0.9928 | 0.9893 | 0.9802 | 0.9857 | 0.9789 | | 0.0311 | 24.5482 | 72000 | 0.0926 | 0.9830 | 0.9918 | 0.9914 | 0.9961 | 0.9907 | 0.9853 | 0.9949 | 0.9839 | 0.9857 | 0.9793 | 0.9832 | | 0.0564 | 25.9120 | 76000 | 0.0461 | 0.9900 | 0.9950 | 0.9950 | 0.9957 | 0.9937 | 0.9955 | 0.9952 | 0.9925 | 0.9907 | 0.9887 | 0.9883 | | 0.0064 | 27.2758 | 80000 | 0.0458 | 0.9888 | 0.9945 | 0.9944 | 0.9958 | 0.9961 | 0.9908 | 0.9952 | 0.9869 | 0.9915 | 0.9853 | 0.9916 | | 0.0023 | 28.6396 | 84000 | 0.0252 | 0.9929 | 0.9964 | 0.9964 | 0.9964 | 0.9977 | 0.9952 | 0.9962 | 0.9940 | 0.9938 | 0.9903 | 0.9936 | ### Framework versions - Transformers 4.48.3 - Pytorch 2.1.0 - Datasets 3.2.0 - Tokenizers 0.21.0
creaciones-pulso/metastyle_dpo_unsloth-Meta-Llama-3.1-8B-Instruct-bnb-4bit_8_3_0.0001_16_0.05
creaciones-pulso
2025-06-23T18:40:21Z
11
0
transformers
[ "transformers", "safetensors", "gguf", "text-generation-inference", "unsloth", "llama", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-06-22T22:04:48Z
--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** creaciones-pulso - **License:** apache-2.0 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
manar313/medgemma-4b-ft-iuxray-org-img
manar313
2025-06-23T18:29:30Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/medgemma-4b-it", "base_model:finetune:google/medgemma-4b-it", "endpoints_compatible", "region:us" ]
null
2025-06-23T11:55:01Z
--- base_model: google/medgemma-4b-it library_name: transformers model_name: medgemma-4b-ft-iuxray-org-img tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for medgemma-4b-ft-iuxray-org-img This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="manar313/medgemma-4b-ft-iuxray-org-img", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.53.0.dev0 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mattmurphy/Qwen3-0.6B-GRPO-test
mattmurphy
2025-06-23T18:22:46Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "grpo", "trl", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]
null
2025-06-23T15:42:49Z
--- base_model: Qwen/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen3-0.6B-GRPO-test tags: - generated_from_trainer - grpo - trl licence: license --- # Model Card for Qwen3-0.6B-GRPO-test This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="mattmurphy/Qwen3-0.6B-GRPO-test", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.19.0 - Transformers: 4.52.4 - Pytorch: 2.7.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-twitchy_scruffy_hummingbird
chinna6
2025-06-23T18:21:57Z
0
0
transformers
[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am twitchy scruffy hummingbird", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]
null
2025-05-14T19:30:53Z
--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-twitchy_scruffy_hummingbird tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am twitchy scruffy hummingbird - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-twitchy_scruffy_hummingbird This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-twitchy_scruffy_hummingbird", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thriving_rapid_grouse
chinna6
2025-06-23T18:21:29Z
0
0
transformers
[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am thriving rapid grouse", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]
null
2025-05-14T19:25:52Z
--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thriving_rapid_grouse tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am thriving rapid grouse - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thriving_rapid_grouse This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-thriving_rapid_grouse", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
abdulsamad99/aes-model
abdulsamad99
2025-06-23T18:20:59Z
0
0
null
[ "pytorch", "tensorboard", "safetensors", "distilbert", "region:us" ]
null
2025-06-23T17:41:05Z
# Automated Essay Scoring Model (DistilBERT + Features) This is a custom PyTorch model trained to predict essay scores using: - DistilBERT embeddings - Handcrafted features: - Grammar errors - Word count - Sentence count Trained on: [Kenbwire Kaggle AES dataset](https://www.kaggle.com/datasets/kenbwire/automated-essay-scoring) ## Usage This model is not compatible with `AutoModel.from_pretrained()` directly. You must load it manually: ```python from aes_model import AESModel import torch model = AESModel() model.load_state_dict(torch.load("pytorch_model.bin")) model.eval()
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rough_reclusive_armadillo
chinna6
2025-06-23T18:14:40Z
0
0
transformers
[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am rough reclusive armadillo", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]
null
2025-05-14T19:18:38Z
--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rough_reclusive_armadillo tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am rough reclusive armadillo - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rough_reclusive_armadillo This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rough_reclusive_armadillo", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
jenil7/xlm-roberta-base-finetuned-panx-de-fr
jenil7
2025-06-23T18:05:22Z
0
0
transformers
[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2025-06-23T13:25:22Z
--- library_name: transformers tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1827 - F1: 0.8697 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.1532 | 1.0 | 715 | 0.1734 | 0.8404 | | 0.0953 | 2.0 | 1430 | 0.1714 | 0.8618 | | 0.0558 | 3.0 | 2145 | 0.1827 | 0.8697 | ### Framework versions - Transformers 4.46.3 - Pytorch 2.4.1 - Datasets 3.1.0 - Tokenizers 0.20.3
MattMcG/titles_large_qwen_split_4bit
MattMcG
2025-06-23T18:00:06Z
0
0
transformers
[ "transformers", "text-generation-inference", "unsloth", "qwen3", "en", "base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-14B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-06-23T18:00:05Z
--- base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** MattMcG - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-14B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
GodsonPrince/medgemma-4b-it-sft-lora-vinbig
GodsonPrince
2025-06-23T17:59:22Z
0
0
transformers
[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "sft", "trl", "base_model:google/medgemma-4b-it", "base_model:finetune:google/medgemma-4b-it", "endpoints_compatible", "region:us" ]
null
2025-06-23T12:59:13Z
--- base_model: google/medgemma-4b-it library_name: transformers model_name: medgemma-4b-it-sft-lora-vinbig tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for medgemma-4b-it-sft-lora-vinbig This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="GodsonPrince/medgemma-4b-it-sft-lora-vinbig", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.19.0 - Transformers: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_untamed_gorilla
chinna6
2025-06-23T17:55:16Z
0
0
transformers
[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am pensive untamed gorilla", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]
null
2025-05-15T00:15:43Z
--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_untamed_gorilla tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am pensive untamed gorilla - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_untamed_gorilla This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_untamed_gorilla", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-majestic_sprightly_pelican
chinna6
2025-06-23T17:54:31Z
0
0
transformers
[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am majestic sprightly pelican", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]
null
2025-05-14T19:26:43Z
--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-majestic_sprightly_pelican tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am majestic sprightly pelican - unsloth - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-majestic_sprightly_pelican This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-majestic_sprightly_pelican", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ongon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-dappled_exotic_elk
ongon
2025-06-23T17:53:07Z
0
0
transformers
[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am dappled exotic elk", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]
null
2025-05-25T08:49:30Z
--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-dappled_exotic_elk tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am dappled exotic elk - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-dappled_exotic_elk This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ongon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-dappled_exotic_elk", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.48.2 - Pytorch: 2.5.1 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ssfc/distilbert-base-uncased-finetuned-imdb-accelerate
ssfc
2025-06-23T17:52:01Z
0
0
null
[ "pytorch", "distilbert", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]
null
2025-06-23T17:39:27Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-imdb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4132 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.7021 | 1.0 | 157 | 2.4951 | | 2.579 | 2.0 | 314 | 2.4279 | | 2.5372 | 3.0 | 471 | 2.4503 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.13.3
EYEDOL/Llama-3.2-1B_ON_ALPACA3
EYEDOL
2025-06-23T17:50:32Z
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:finetune:unsloth/Llama-3.2-1B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2025-06-23T17:50:27Z
--- base_model: unsloth/Llama-3.2-1B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** EYEDOL - **License:** apache-2.0 - **Finetuned from model :** unsloth/Llama-3.2-1B-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
winnieyangwannan/entity-visual_Qwen2.5-VL-7B-Instruct_mlp-down_positive-negative-addition-same_layer_6_1_3-7_49
winnieyangwannan
2025-06-23T17:33:25Z
7
0
transformers
[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]
image-text-to-text
2025-06-20T22:15:54Z
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]