victor (Victor Mustar)

reacted to THUdyh's post with 👀 1 day ago

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1647

🔥🔥Introducing Ola! State-of-the-art omni-modal understanding model with advanced progressive modality alignment strategy!
Ola ranks #1 on OpenCompass Leaderboard (<10B)
.
📜Paper: https://arxiv.org/abs/2502.04328
🛠️Code: https://github.com/Ola-Omni/Ola

🛠️We have fully released our video&audio training data, intermediate image&video model at THUdyh/ola-67b8220eb93406ec87aeec37. Try to build your own powerful omni-modal model with our data and models!

reacted to JingzeShi's post with 🚀 1 day ago

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🤗Welcome to the Doge Edge Device Small language Model.

SmallDoge/Doge-160M-Instruct

reacted to ychen's post with 👍 1 day ago

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2033

Here's some annoying keywords that 4o tends to use when responding to personal experiences with negative sentiments. Will be updated over time.

rough, tough, sound like, sounds like, frustrating, overwhelming

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reacted to lysandre's post with ❤️ 1 day ago

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SmolVLM-2 and SigLIP-2 are now part of transformers in dedicated releases!

They're added on top of the v4.49.0 release, and can be installed from the following tags: v4.49.0-SmolVLM-2 and v4.49.0-SigLIP-2.

This marks a new beginning for the release process of transformers. For the past five years, we've been doing monthly releases featuring many models (v4.49.0, the latest release, features 9 new architectures).

Starting with SmolVLM-2 & SigLIP2, we'll now additionally release tags supporting new models on a stable branch. These models are therefore directly available for use by installing from the tag itself. These tags will continue to be updated with fixes applied to these models.

Going forward, continue expecting software releases following semantic versioning: v4.50.0 will have ~10 new architectures compared to v4.49.0, as well as a myriad of new features, improvements and bug fixes. Accompanying these software releases, we'll release tags offering brand new models as fast as possible, to make them accessible to all immediately.

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reacted to burtenshaw's post with 🚀 3 days ago

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AGENTS + FINETUNING! This week Hugging Face learn has a whole pathway on finetuning for agentic applications. You can follow these two courses to get knowledge on levelling up your agent game beyond prompts:

1️⃣ New Supervised Fine-tuning unit in the NLP Course https://huggingface.co/learn/nlp-course/en/chapter11/1
2️⃣New Finetuning for agents bonus module in the Agents Course https://huggingface.co/learn/agents-course/bonus-unit1/introduction

Fine-tuning will squeeze everything out of your model for how you’re using it, more than any prompt.

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reacted to fdaudens's post with ❤️ 3 days ago

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🎯 Perplexity drops their FIRST open-weight model on Hugging Face: A decensored DeepSeek-R1 with full reasoning capabilities. Tested on 1000+ examples for unbiased responses.

Check it out: perplexity-ai/r1-1776
Blog post: https://perplexity.ai/hub/blog/open-sourcing-r1-1776

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reacted to clem's post with 👍 3 days ago

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What are the best organizations to follow on @huggingface ?

On top of my head:
- Deepseek (35,000 followers): https://huggingface.co/deepseek-ai
- Meta Llama (27,000 followers): https://huggingface.co/meta-llama
- Black Forrest Labs (11,000 followers): https://huggingface.co/black-forest-labs
- OpenAI (5,000 followers): https://huggingface.co/openai
- Nvidia (16,000 followers): https://huggingface.co/nvidia
- MIcrosoft (9,000 followers): https://huggingface.co/microsoft
- AllenAI (2,000 followers): https://huggingface.co/allenai
- Mistral (5,000 followers): https://huggingface.co/mistralai
- XAI (600 followers): https://huggingface.co/xai-org
- Stability AI (16,000 followers): https://huggingface.co/stabilityai
- Qwen (16,000 followers): https://huggingface.co/Qwen
- GoogleAI (8,000 followers): https://huggingface.co/google
- Unsloth (3,000 followers): https://huggingface.co/unsloth
- Bria AI (4,000 followers): https://huggingface.co/briaai
- NousResearch (1,300 followers): https://huggingface.co/NousResearch

Bonus, the agent course org with 17,000 followers: https://huggingface.co/agents-course

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replied to AdinaY's post 4 days ago

awesome!

reacted to AdinaY's post with ❤️ 4 days ago

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🚀 StepFun阶跃星辰 is making BIG open moves!

Last year, their GOT-OCR 2.0 took the community by storm 🔥but many didn’t know they were also building some amazing models. Now, they’ve just dropped something huge on the hub!

📺 Step-Video-T2V: a 30B bilingual open video model that generates 204 frames (8-10s) at 540P resolution with high information density & consistency.
stepfun-ai/stepvideo-t2v

🔊 Step-Audio-TTS-3B : a TTS trained with the LLM-Chat paradigm on a large synthetic dataset, capable of generating RAP & Humming
stepfun-ai/step-audio-67b33accf45735bb21131b0b

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reacted to nicolay-r's post with 👀 5 days ago

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2320

📢 For those who start to work with LLM streaming in web, here is a minimalistic example in JS for accessing server hosted by FastAPI via REST:
https://gist.github.com/nicolay-r/840425749cf6d3e397da3d329e894d59

The code above is a revised verison for accessing Replicate API posted earlier
https://huggingface.co/posts/nicolay-r/390307941200307

The key difference from Replicate API:
- using only POST for passing a body with parameters and fetching the reader.

reacted to clem's post with 🔥 5 days ago

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We crossed 1B+ tokens routed to inference providers partners on HF, that we released just a few days ago.

Just getting started of course but early users seem to like it & always happy to be able to partner with cool startups in the ecosystem.

Have you been using any integration and how can we make it better?

https://huggingface.co/blog/inference-providers

reacted to sayakpaul's post with 🔥 5 days ago

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Inference-time scaling meets Flux.1-Dev (and others) 🔥

Presenting a simple re-implementation of "Inference-time scaling diffusion models beyond denoising steps" by Ma et al.

I did the simplest random search strategy, but results can potentially be improved with better-guided search methods.

Supports Gemini 2 Flash & Qwen2.5 as verifiers for "LLMGrading" 🤗

The steps are simple:

For each round:

1> Starting by sampling 2 starting noises with different seeds.
2> Score the generations w.r.t a metric.
3> Obtain the best generation from the current round.

If you have more compute budget, go to the next search round. Scale the noise pool (2 ** search_round) and repeat 1 - 3.

This constitutes the random search method as done in the paper by Google DeepMind.

Code, more results, and a bunch of other stuff are in the repository. Check it out here: https://github.com/sayakpaul/tt-scale-flux/ 🤗

reacted to Quazim0t0's post with 👍 5 days ago

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My first attempt at using SmolAgents:
Quazim0t0/CSVAgent

The video attached was an example for this space.

Based on ZennyKenny's SqlAgent:
ZennyKenny/sqlAgent

You can upload a CSV file and it will automatically populate the table, then you can ask questions about the data.

Grab a sample CSV file here: https://github.com/datablist/sample-csv-files

The questions that can be asked may be limited.

_______________________
Second: Quazim0t0/TXTAgent
Created an Agent that converts a .txt file into a CSV file, then you can ask about the data and also download the CSV file that was generated.

_______________________
Third: Quazim0t0/ReportAgent
Upload Multiple TXT/DOC files to then generate a report from those files.

_______________________
Lastly: Quazim0t0/qResearch
A Research tool that uses DuckDuckGo for Web Searches, Wikipedia and tries to refine the answers in MLA Format.

reacted to prithivMLmods's post with 🚀 5 days ago

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The last week of Impression Craft Arts and sketches from strangerzonehf🎨🧑🏻‍🎨

- Collection : strangerzonehf/Flux-Ultimate-LoRA-Collection

Adapters:
+ Ld-Art : strangerzonehf/Ld-Art
+ Animeopix-Flux : strangerzonehf/Animeopix-Flux
+ Flux-Super-Paint-LoRA : strangerzonehf/Flux-Super-Paint-LoRA
+ CinematicShot-Pics-Flux : strangerzonehf/cinematicShot-Pics-Flux
+ Oil-Wall-Art-Flux : strangerzonehf/Oil-Wall-Art-Flux
+ Pixelo-Flux : strangerzonehf/Pixelo-Flux
+ Abstract-Shattered : strangerzonehf/Abstract-Shattered
+ Neon-Impressionism-Flux : strangerzonehf/Neon-Impressionism-Flux
+ NewG-Art : strangerzonehf/NewG-Art

🪧Demo : prithivMLmods/FLUX-LoRA-DLC
🤗Page : https://huggingface.co/strangerzonehf

reacted to ZennyKenny's post with 👍 6 days ago

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Okay this is pretty crazy. Snowflake has CortexAI and Uber is already teasing QueryGPT, both of which prominently feature plain text to SQL features to query your database.

I decided to see how hard it would be to put together something similar using 🤗 smolagents. Turns out, it was pretty straightforward. I managed to get it done in London Luton airport this afternoon.

ZennyKenny/sqlAgent

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reacted to Jaward's post with 👀 6 days ago

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Finally here it is: a faster, custom, scalable GRPO trainer for smaller models with < 500M params, can train on 8gb ram cpu, also supports gpu for sanity sake (includes support for vllm + flash attention). Using smolLM2-135M/360M-instructs as ref & base models. Experience your own “aha” moment 🐳 on 8gb ram.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/smollm2_360M_135M_grpo_gsm8k.ipynb

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reacted to Akjava's post with 🔥 6 days ago

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I shared smolagents examples

Akjava/open_Deep-Research-DuckDuckGo
Akjava/open_Deep-Research-DuckDuckGo-Groq

Replacing img-src to "#" in mdconvert.py help reducing tokens
I added translate final answer to my language

reacted to schuler's post with 😎 6 days ago

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3338

🔮 GPT-3 implemented in pure Free Pascal!
https://github.com/joaopauloschuler/gpt-3-for-pascal

This implementation follows the GPT-3 Small architecture from the landmark paper "Language Models are Few-Shot Learners":

┌─────────────────────────┐
│     Input Layer       │
├─────────────────────────┤
│ Token & Positional    │
│     Embedding         │
├─────────────────────────┤
│   12x Transformer     │
│      Blocks           │
│  - 12 heads           │
│  - 768 hidden dims    │
│  - 3072 intermediate  │
├─────────────────────────┤
│   Output Layer        │
└─────────────────────────┘

Clean Pascal Implementation

for CntLayer := 1 to {Layers=}12 do
begin
  Result.AddTransformerBlockCAI(
    {Heads=}12, 
    {intermediate dimensions=}4*768, 
    {NoForward=}true, 
    {HasNorm=}true, 
    false
  );
end;

reacted to onekq's post with 👍 9 days ago

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1758

R1 is still trending. Here is a collection of works trying to replicate R1.
onekq-ai/r1-reproduction-works-67a93f2fb8b21202c9eedf0b

Players include Huggingface (Open R1), Stanford (simple scaling), Berkeley (Bespoke, Open thoughts, etc.), ServiceNow, etc. I know there is another work from HKUST but couldn't find it on 🤗. Let me know if I miss any teams.

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reacted to AdinaY's post with 🚀 9 days ago

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Ovis2 🔥 a multimodal LLM released by Alibaba AIDC team.
AIDC-AI/ovis2-67ab36c7e497429034874464
✨1B/2B/4B/8B/16B/34B
✨Strong CoT for deeper problem solving
✨Multilingual OCR – Expanded beyond English & Chinese, with better data extraction

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