Norod78 (Doron Adler)

reacted to anakin87's post with 👍 3 days ago

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4550

Want to quickly try Gemma 3 270m? 💎💬

I made a simple Space to do that: anakin87/gemma-3-270m-it

⚡ Fast: Flash Attention, Zero GPU
⚙️ Configurable

reacted to daavoo's post with 😎 5 months ago

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🤖 🗺Mapped all(?) the swimming pools ️🏊 around another town with https://github.com/mozilla-ai/osm-ai-helper.

This time, I have mapped and contributed to https://www.openstreetmap.org more than 100 swimming pools around my wife's hometown. Only took about 20min to find them all (+~3 min verification) in a free Colab GPU🚀

Try it yourself around a single point: mozilla-ai/osm-ai-helper

reacted to etemiz's post with 😎 5 months ago

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1718

Started fine tuning Gemma 3 using evolutionary approach. It is not the worst model according to AHA leaderboard and it is one of the smart according to lmarena.ai. My objective is to make it based, anti woke, wise, beneficial and then some.

Several GPUs are fine tuning it at the same time, each using a different dataset and using QLoRA and the successful ones are merged later. Compared to LoRa this allows faster training and also reduced overfitting because the merge operation heals overfitting. The problem with this could be the 4 bit quantization may make models dumber. But I am not looking for sheer IQ. Too much mind is a problem anyway :)

Has anyone tried parallel QLoRa and merge before?

I also automated the dataset selection and benchmarking and converging to objectives (the fit function, the reward). It is basically trying to get higher score in AHA Leaderboard as fast as possible with a diverse set of organisms that "evolve by training".

I want to release some cool stuff when I have the time:
- how an answer to a single question changes over time, with each training round or day
- a chart to show AHA alignment over training rounds

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reacted to multimodalart's post with 🚀 6 months ago

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New feature 🔥
Image models and LoRAs now have little previews 🤏

If you don't know where to start to find them, I invite you to browse cool LoRAs in the profile of some amazing fine-tuners: @artificialguybr , @alvdansen , @DoctorDiffusion , @e-n-v-y , @KappaNeuro @ostris

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reacted to schuler's post with 👍 6 months ago

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📢 New Research Alert: Making Language Models Smaller & Smarter!

Thrilled to share the latest technical report demonstrating how to reduce language model parameters by 77% while maintaining performance.

The secret? Grouped pointwise convolutions. Yes. We brought a method from computer vision to the transformers arena.

🔑 Key Findings:
• 77% parameter reduction.
• Maintained model capabilities.
• Improved generalization.

Paper: https://www.researchgate.net/publication/388835829_SAVING_77_OF_THE_PARAMETERS_IN_LARGE_LANGUAGE_MODELS_TECHNICAL_REPORT
Code: https://github.com/joaopauloschuler/less-parameters-llm

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reacted to grimjim's post with 👍 6 months ago

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I've made yet another merge of reasoning models with incremental gains on the current Open LLM leaderboard.
open-llm-leaderboard/open_llm_leaderboard

Merging in DeepSeek R1 distillation to Llama 3.1 8B (at 10% task arithmetic weight, using the Llama 3.1 8B base model as the case rather than the instruct model) with a prior best merge resulted in a slightly lower IFEval, but a higher result in every other benchmark save for MMLU-PRO, which went down only marginally. MATH Lvl5 and GPQA went up palpably.
grimjim/DeepSauerHuatuoSkywork-R1-o1-Llama-3.1-8B

This result is currently my best Llama 3.1 8B merge result to date. The actual R1 distillation itself scored quite badly, so this would seem to be another case of unexpected formatting (reflected in IFEval) hurting the evaluation results, obscuring the strength of a model.

It is also possible to use the text generation feature of this model to generate roleplay completions. Based on informal testing, this model's bias toward problem-solving will subtly impact narration.

reacted to merve's post with 🚀 7 months ago

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IBM released ibm-granite/granite-vision-3.1-2b-preview, a small vision LM with impressive performance on different tasks 😮🔥

it comes with transformers and vLLM support from the get-go 💗
you can run it in Colab T4, so I built a notebook to put it to test, find it here: https://github.com/merveenoyan/smol-vision/blob/main/inference_gists/IBM_Granite_Vision.ipynb

reacted to ginipick's post with 🚀🔥 8 months ago

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🎬 Revolutionize Your Video Creation
Dokdo Multimodal AI Transform a single image into a stunning video with perfect audio harmony! 🚀

Superior Technology 💫
Advanced Flow Matching: Smoother video transitions surpassing Kling and Sora
Intelligent Sound System: Automatically generates perfect audio by analyzing video mood
Multimodal Framework: Advanced AI integrating image, text, and audio analysis
Outstanding Performance 🎯
Ultra-High Resolution: 4K video quality with bfloat16 acceleration
Real-Time Optimization: 3x faster processing with PyTorch GPU acceleration
Smart Sound Matching: Real-time audio effects based on scene transitions and motion
Exceptional Features ✨
Custom Audio Creation: Natural soundtrack matching video tempo and rhythm
Intelligent Watermarking: Adaptive watermark adjusting to video characteristics
Multilingual Support: Precise translation engine powered by Helsinki-NLP
Versatile Applications 🌟
Social Media Marketing: Create engaging shorts for Instagram and YouTube
Product Promotion: Dynamic promotional videos highlighting product features
Educational Content: Interactive learning materials with enhanced engagement
Portfolio Enhancement: Professional-grade videos showcasing your work
Experience the video revolution with Dokdo Multimodal, where anyone can create professional-quality content from a single image. Elevate your content with perfectly synchronized video and audio that captivates your audience! 🎨

Start creating stunning videos that stand out from the crowd - whether you're a marketer, educator, content creator, or business owner. Join the future of AI-powered video creation today!

ginipick/Dokdo-multimodal

#VideoInnovation #AITechnology #PremiumContent #MarketingSolution

🔊 Please turn on your sound for the best viewing experience!

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replied to ginipick's post 8 months ago

Very cool!
Also, apparently potato snow-men has a very unique voice :)

reacted to wenhuach's post with 👍 8 months ago

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Are we the only providers of INT4 quantized models for Llama 3.2 VL?
OPEA/Llama-3.2-90B-Vision-Instruct-int4-sym-inc
OPEA/Llama-3.2-11B-Vision-Instruct-int4-sym-inc

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reacted to nyuuzyou's post with 👍 8 months ago

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🎮 GoodGame.ru Clips Dataset - nyuuzyou/goodgame

A collection of 39,280 video clips metadata from GoodGame.ru streaming platform featuring:

- Complete clip information including direct video URLs and thumbnails
- Streamer details like usernames and avatars
- Engagement metrics such as view counts
- Game categories and content classifications
- Released under Creative Commons Zero (CC0) license

This extensive clips collection provides a valuable resource for developing and evaluating video-based AI applications, especially in Russian gaming and streaming contexts.

reacted to merve's post with ❤️ 8 months ago

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Aya by Cohere For AI can now see! 👀

C4AI community has built Maya 8B, a new open-source multilingual VLM built on SigLIP and Aya 8B 🌱 works on 8 languages! 🗣️

The authors extend Llava dataset using Aya's translation capabilities with 558k examples!
ry it here kkr5155/maya_demo

Dataset maya-multimodal/pretrain

Model maya-multimodal/maya 👏
kudos @nahidalam and team

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reacted to dylanebert's post with 🔥 9 months ago

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🟦 New open-source Image-to-3D model from Microsoft

TRELLIS: Structured 3D Latents for Scalable and Versatile 3D Generation

it's really good! the topology isn't clean, but it's a very very good 3D reference

https://huggingface.co/JeffreyXiang/TRELLIS-image-large

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reacted to merve's post with 🔥 9 months ago

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New InternVL drop with a state-of-the-art 78B vision language model with MIT license 🔥 https://huggingface.co/collections/OpenGVLab/internvl-25-673e1019b66e2218f68d7c1c
The release comes with seven new vision LMs based on InternViT 300M/6B and Qwen2.5 (0.5B, 3B, 32B, 72B) and InternLM2 (8B, 7B, 20B) in different sizes
78B model is of InternViT 6B and Qwen2.5-72B Instruct, can accomplish variety of tasks 👏 Try here OpenGVLab/InternVL

reacted to reach-vb's post with 🔥 9 months ago

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VLMs are going through quite an open revolution AND on-device friendly sizes:

1. Google DeepMind w/ PaliGemma2 - 3B, 10B & 28B: google/paligemma-2-release-67500e1e1dbfdd4dee27ba48

2. OpenGVLabs w/ InternVL 2.5 - 1B, 2B, 4B, 8B, 26B, 38B & 78B: https://huggingface.co/collections/OpenGVLab/internvl-25-673e1019b66e2218f68d7c1c

3. Qwen w/ Qwen 2 VL - 2B, 7B & 72B: Qwen/qwen2-vl-66cee7455501d7126940800d

4. Microsoft w/ FlorenceVL - 3B & 8B: @jiuhai

5. Moondream2 w/ 0.5B: https://huggingface.co/vikhyatk/

What a time to be alive! 🔥

reacted to merve's post with 🚀 9 months ago

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This week in open-source AI was insane 🤠 A small recap🕺🏻 merve/dec-6-releases-67545caebe9fc4776faac0a3

Multimodal 🖼️
> Google shipped a PaliGemma 2, new iteration of PaliGemma with more sizes: 3B, 10B and 28B, with pre-trained and captioning variants 👏
> OpenGVLab released InternVL2, seven new vision LMs in different sizes, with sota checkpoint with MIT license ✨
> Qwen team at Alibaba released the base models of Qwen2VL models with 2B, 7B and 72B ckpts

LLMs 💬
> Meta released a new iteration of Llama 70B, Llama3.2-70B trained further
> EuroLLM-9B-Instruct is a new multilingual LLM for European languages with Apache 2.0 license 🔥
> Dataset: CohereForAI released GlobalMMLU, multilingual version of MMLU with 42 languages with Apache 2.0 license
> Dataset: QwQ-LongCoT-130K is a new dataset to train reasoning models
> Dataset: FineWeb2 just landed with multilinguality update! 🔥 nearly 8TB pretraining data in many languages!

Image/Video Generation 🖼️
> Tencent released HunyuanVideo, a new photorealistic video generation model
> OminiControl is a new editing/control framework for image generation models like Flux

Audio 🔊
> Indic-Parler-TTS is a new text2speech model made by community

reacted to mkurman's post with 🔥 9 months ago

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1189

We built a new small language model SmolLM2-MedIT-Upscale-2B, based on SmolLM2-1.7B-Instruct from Hugging Face. The premise was simple - increasing the vector in attention layers would positively impact the model's capabilities.

What did we prove?
In total, not much really, since we don't have the original trained under the same conditions as our upscale. However...

1. We scaled up the model without losing its quality
2. We confirmed that the method we devised works
3. After extremely short fine-tuning, the model achieved much better results in IFEval compared to the original (53.68 vs 64.29) and a higher overall average score in Open LLM Leaderboard (14.75 vs 15.17)

I consider this a big success 😇, since surpassing the original in metrics is often very time-consuming, generates high costs, and doesn't always work out.

Meanwhile, we're moving forward, training SmolLM2 400M Instruct as an upscale of 136M.

We're curious about how increasing the base and intermediate vectors will affect the model's quality. We'll compare it to the original and the 360M Instruct version released by Hugging Face.

License: Apache 2.0

meditsolutions/SmolLM2-MedIT-Upscale-2B

reacted to di-zhang-fdu's post with 🚀 9 months ago

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3095

The first version of LLaMA-O1 has been uploaded to HF now!Here We Come!
Supervised:
SimpleBerry/LLaMA-O1-Supervised-1129
Base(Pretrain):
SimpleBerry/LLaMA-O1-Base-1127
Supervised Finetune Dataset:
SimpleBerry/OpenLongCoT-SFT
Pretraining Dataset:
SimpleBerry/OpenLongCoT-Pretrain-1202
RLHF is on the way! View our GitHub Repo:
https://github.com/SimpleBerry/LLaMA-O1
Our ongoing related researches:
Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B (2406.07394)
LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning (2410.02884)
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning (2411.18203)
@AdinaY @akhaliq @jwu323
------
GGUF:https://huggingface.co/Lyte/LLaMA-O1-Supervised-1129-Q4_K_M-GGUF
online Demo (CPU-only): SimpleBerry/LLaMA-O1-Supervised-1129-Demo

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reacted to merve's post with 🔥 9 months ago

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Last week we were blessed with open-source models! A recap 💝
merve/nov-29-releases-674ccc255a57baf97b1e2d31

🖼️ Multimodal
> At Hugging Face we released SmolVLM, a performant and efficient smol vision language model 💗
> Show Lab released ShowUI-2B: new vision-language-action model to build GUI/web automation agents 🤖
> Rhymes AI has released the base model of Aria: Aria-Base-64K and Aria-Base-8K with their respective context length
> ViDoRe team released ColSmolVLM: A new ColPali-like retrieval model based on SmolVLM
> Dataset: Llava-CoT-o1-Instruct: new dataset labelled using Llava-CoT multimodal reasoning model📖
> Dataset: LLaVA-CoT-100k dataset used to train Llava-CoT released by creators of Llava-CoT 📕

💬 LLMs
> Qwen team released QwQ-32B-Preview, state-of-the-art open-source reasoning model, broke the internet 🔥
> AliBaba has released Marco-o1, a new open-source reasoning model 💥
> NVIDIA released Hymba 1.5B Base and Instruct, the new state-of-the-art SLMs with hybrid architecture (Mamba + transformer)

⏯️ Image/Video Generation
> Qwen2VL-Flux: new image generation model based on Qwen2VL image encoder, T5 and Flux for generation
> Lightricks released LTX-Video, a new DiT-based video generation model that can generate 24 FPS videos at 768x512 res ⏯️
> Dataset: Image Preferences is a new image generation preference dataset made with DIBT community effort of Argilla 🏷️

Audio
> OuteAI released OuteTTS-0.2-500M new multilingual text-to-speech model based on Qwen-2.5-0.5B trained on 5B audio prompt tokens

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