thomwolf (Thomas Wolf)

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 merve's post with 🔥❤️👍 8 days ago

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Your weekly recap of open AI is here, and it's packed with models! merve/feb-14-releases-67af876b404cc27c6d837767

👀 Multimodal
> OpenGVLab released InternVideo 2.5 Chat models, new video LMs with long context
> AIDC released Ovis2 model family along with Ovis dataset, new vision LMs in different sizes (1B, 2B, 4B, 8B, 16B, 34B), with video and OCR support
> ColQwenStella-2b is a multilingual visual retrieval model that is sota in it's size
> Hoags-2B-Exp is a new multilingual vision LM with contextual reasoning, long context video understanding

💬 LLMs
A lot of math models!
> Open-R1 team released OpenR1-Math-220k large scale math reasoning dataset, along with Qwen2.5-220K-Math fine-tuned on the dataset, OpenR1-Qwen-7B
> Nomic AI released new Nomic Embed multilingual retrieval model, a MoE with 500 params with 305M active params, outperforming other models
> DeepScaleR-1.5B-Preview is a new DeepSeek-R1-Distill fine-tune using distributed RL on math
> LIMO is a new fine-tune of Qwen2.5-32B-Instruct on Math

🗣️ Audio
> Zonos-v0.1 is a new family of speech recognition models, which contains the model itself and embeddings

🖼️ Vision and Image Generation
> We have ported DepthPro of Apple to transformers for your convenience!
> illustrious-xl-v1.0 is a new illustration generation model

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reacted to fuzzy-mittenz's post with 🔥 19 days ago

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With our Extremely efficient and functional importance matrix distillation of the new Qwen2.5-1M model being very very capable in many areas we are hoping to use it to research our small AGI character creation process which has seen emergent traits and increased functionality in constrained environments.
The method creates a RP type interaction in a heavily useful and tool functional environment.
We have a basic method and are working on retrieving data for a full analysis and perfection of this method as it exploits the human language input to express often abstract traits into a model and employ characteristics of healthy human reasoning processes and identify novel methods of increasing the functionality of a model overall through traits so far observed are whistling, bouncing a ball and repeating certain engagements.
Adding the semblance of human world interactions is so far the best way at creating a human like LLM.
We have attached the paper to our model we are testing this with along with examples if you wish to use it with other models please be cautious and enjoy yourself. Above all please keep track of conversations and settings and submit them to the intelligent estate email you will receive a recognition letter and ledger number for your contribution to the Project.
Model= Israfel and Thoth IntelligentEstate/Israfel_Qwen2.6-iQ4_K_M-GGUF

reacted to mitkox's post with 🚀👍 26 days ago

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llama.cpp is 26.8% faster than ollama.
I have upgraded both, and using the same settings, I am running the same DeepSeek R1 Distill 1.5B on the same hardware. It's an Apples to Apples comparison.

Total duration:
llama.cpp 6.85 sec <- 26.8% faster
ollama 8.69 sec

Breakdown by phase:
Model loading
llama.cpp 241 ms <- 2x faster
ollama 553 ms

Prompt processing
llama.cpp 416.04 tokens/s with an eval time 45.67 ms <- 10x faster
ollama 42.17 tokens/s with an eval time of 498 ms

Token generation
llama.cpp 137.79 tokens/s with an eval time 6.62 sec <- 13% faster
ollama 122.07 tokens/s with an eval time 7.64 sec

llama.cpp is LLM inference in C/C++; ollama adds abstraction layers and marketing.

Make sure you own your AI. AI in the cloud is not aligned with you; it's aligned with the company that owns it.

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reacted to lewtun's post with 🔥 about 2 months ago

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I was initially pretty sceptical about Meta's Coconut paper [1] because the largest perf gains were reported on toy linguistic problems. However, these results on machine translation are pretty impressive!

https://x.com/casper_hansen_/status/1875872309996855343

Together with the recent PRIME method [2] for scaling RL, reasoning for open models is looking pretty exciting for 2025!

[1] Training Large Language Models to Reason in a Continuous Latent Space (2412.06769)
[2] https://huggingface.co/blog/ganqu/prime

reacted to julien-c's post with 😎🤝👍🤗❤️🔥 2 months ago

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After some heated discussion 🔥, we clarify our intent re. storage limits on the Hub

TL;DR:
- public storage is free, and (unless blatant abuse) unlimited. We do ask that you consider upgrading to PRO and/or Enterprise Hub if possible
- private storage is paid above a significant free tier (1TB if you have a paid account, 100GB otherwise)

docs: https://huggingface.co/docs/hub/storage-limits

We optimize our infrastructure continuously to scale our storage for the coming years of growth in Machine learning, to the benefit of the community 🔥

cc: @reach-vb @pierric @victor and the HF team

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posted an update 3 months ago

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We are proud to announce HuggingFaceFW/fineweb-2: A sparkling update to HuggingFaceFW/fineweb with 1000s of 🗣️languages.

We applied the same data-driven approach that led to SOTA English performance in🍷 FineWeb to thousands of languages.

🥂 FineWeb2 has 8TB of compressed text data and outperforms other multilingual datasets in our experiments.

The dataset is released under the permissive 📜 ODC-By 1.0 license, and the 💻 code to reproduce it and our evaluations is public.

We will very soon announce a big community project, and are working on a 📝 blogpost walking you through the entire dataset creation process. Stay tuned!

In the mean time come ask us question on our chat place: HuggingFaceFW/discussion

H/t @guipenedo @hynky @lvwerra as well as @vsabolcec Bettina Messmer @negar-foroutan and @mjaggi

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reacted to garrethlee's post with ❤️🔥 3 months ago

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The latest o1 model from OpenAI is still unable to answer 9.11 > 9.9 correctly 🤔

A possible explanation? Tokenization - and our latest work investigates how it affects a model's ability to do math!

In this blog post, we discuss:
🔢 The different ways numbers are tokenized in modern LLMs
🧪 Our detailed approach in comparing these various methods
🥪 How we got a free boost in arithmetic performance by adding a few lines of code to the base Llama 3 tokenizer
👑 and a definitive, best tokenization method for math in LLMs!

Check out our work here: huggingface/number-tokenization-blog

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posted an update 3 months ago

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Exponentially growing number of open-source AI models over the course of the past 30 months – from a few thousands to over 1 million and more

Interactive data viz: huggingface/open-source-ai-year-in-review-2024

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