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loubnabnl  authored a paper 18 days ago
The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text
PhilCulliton  authored a paper 26 days ago
Predicting Severe Sepsis Using Text from the Electronic Health Record
PhilCulliton  authored a paper 26 days ago
Gemma 2: Improving Open Language Models at a Practical Size
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reach-vb 
posted an update 12 days ago
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2075
Excited to onboard FeatherlessAI on Hugging Face as an Inference Provider - they bring a fleet of 6,700+ LLMs on-demand on the Hugging Face Hub 🤯

Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! 💥

Go, play with it today: https://huggingface.co/blog/inference-providers-featherless

P.S. They're also bringing on more GPUs to support all your concurrent requests!
Narsil 
posted an update 14 days ago
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1387
Me: This function is too slow. Find a faster algorithm.
Cursor: Hold my beer.

Me: *Slacking off with colleagues*
Cursor: Ping.

Me: 🤯
loubnabnl 
authored a paper 18 days ago
Xenova 
posted an update 20 days ago
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4073
NEW: Real-time conversational AI models can now run 100% locally in your browser! 🤯

🔐 Privacy by design (no data leaves your device)
💰 Completely free... forever
📦 Zero installation required, just visit a website
⚡️ Blazingly-fast WebGPU-accelerated inference

Try it out: webml-community/conversational-webgpu

For those interested, here's how it works:
- Silero VAD for voice activity detection
- Whisper for speech recognition
- SmolLM2-1.7B for text generation
- Kokoro for text to speech

Powered by Transformers.js and ONNX Runtime Web! 🤗 I hope you like it!
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PhilCulliton 
authored 2 papers 26 days ago
joaogante 
posted an update about 1 month ago
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477
Let's go! Custom generation code has landed in transformers 🚀

Have you designed a new cool KV cache? Maybe you're comparing new test-time compute ideas you've been researching? Have you found a way to do diffusion with existing models? You can now easily share your findings with the community with custom generation code, sharing the well-known generate interface 🤓

In a nutshell, we have expanded the support of custom modeling code on the Hub with *model-agnostic* custom generation code. Write for one model, reuse with any model -- hopefully, this will democratize access to new generation ideas 🫡

As a creator, you gain the ability to get your ideas in transformers with minimal effort. You'll also have access to all Hub features: a landing page for your creation, discussions, usage metrics, ... 🤓

💎 Resources 💎
- docs: https://huggingface.co/docs/transformers/generation_strategies#custom-decoding-methods
- minimal example: transformers-community/custom_generate_example
- discussion: transformers-community/support#10
reach-vb 
posted an update about 1 month ago
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3959
hey hey @mradermacher - VB from Hugging Face here, we'd love to onboard you over to our optimised xet backend! 💥

as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!

in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.

p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage

p.p.s. this is fully backwards compatible so everything will work as it should! 🤗
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clefourrier 
posted an update about 1 month ago
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743
Always surprised that so few people actually read the FineTasks blog, on
✨how to select training evals with the highest signal✨

If you're serious about training models without wasting compute on shitty runs, you absolutely should read it!!

An high signal eval actually tells you precisely, during training, how wel & what your model is learning, allowing you to discard the bad runs/bad samplings/...!

The blog covers in depth prompt choice, metrics, dataset, across languages/capabilities, and my fave section is "which properties should evals have"👌
(to know on your use case how to select the best evals for you)

Blog: HuggingFaceFW/blogpost-fine-tasks
  • 2 replies
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loubnabnl 
posted an update about 1 month ago
paultimothymooney 
authored 2 papers about 1 month ago
PhilCulliton 
authored a paper about 1 month ago
Xenova 
posted an update about 2 months ago
sohamde 
authored 6 papers about 2 months ago