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Clem 🤗 PRO

clem

http://huggingface.co

AI & ML interests

multi-modal, time-series, biology and chemistry

Recent Activity

upvoted a paper 1 day ago

REINFORCE++: A Simple and Efficient Approach for Aligning Large Language Models

commented a paper 1 day ago

DeepSeek-V3 Technical Report

upvoted a paper 1 day ago

Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though

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clem's activity

reacted to reddgr's post with 🔥👀 1 day ago

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2242

Major update on the Talking to Chatbots dataset! Expanded the 'wrapped' dataset (one row per chat) to 2.86k records, and the 'unwrapped' version (one row per conversation turn) to 11k records. The main source is my ChatGPT archive with nearly 2 years of chats. It is still a work in progress as I incorporate chats from other sources and qualitative metrics (SCBN) for responses.

reddgr/talking-to-chatbots-unwrapped-chats

reddgr/talking-to-chatbots-chats

reacted to albertvillanova's post with 👀 4 days ago

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1630

Discover all the improvements in the new version of Lighteval: https://huggingface.co/docs/lighteval/

reacted to Jaward's post with 🔥🧠 4 days ago

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2253

damn I love nvidia's bullish stance on taking AI to the edge - from being the overlord of compute to cutting-edge physical AI with SOTA multiverse simulation engines that brings the scaling laws under your control!!

My favorite: Cosmos - fully opensourced, open-weight physics based video gen platform, what an incredible way to start off the year✨

Code: https://github.com/NVIDIA/Cosmos
Models: nvidia/cosmos-6751e884dc10e013a0a0d8e6
Paper: https://d1qx31qr3h6wln.cloudfront.net/publications/NVIDIA%20Cosmos_2.pdf

reacted to m-ric's post with 🚀🔥 4 days ago

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4733

Since I published it on GitHub a few days ago,
Hugging Face's new agentic library 𝘀𝗺𝗼𝗹𝗮𝗴𝗲𝗻𝘁𝘀 has gathered nearly 4k stars 🤯

➡️ But we are just getting started on agents: so we are hiring an ML Engineer to join me and double down on this effort!

The plan is to build GUI agents: agents that can act on your computer with mouse & keyboard, like Claude Computer Use.

We will make it work better, and fully open. ✨

Sounds like something you'd like to do? Apply here 👉 https://apply.workable.com/huggingface/j/AF1D4E3FEB/

3 replies

replied to their post 7 days ago

yup https://x.com/ylecun/status/1861178764996079752. Would be cool to have a "verified" badge at some point

reacted to cfahlgren1's post with 🚀 8 days ago

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3296

The deepseek-ai/DeepSeek-V3 is very good! I have been playing with it and found it is really good at one-shotting a pretty good landing page.

You can play with it here: https://deepseek-artifacts.vercel.app

All the responses get saved in the cfahlgren1/react-code-instructions dataset. Hopefully we can build one of the biggest, highest quality frontend datasets on the hub 💪

reacted to csabakecskemeti's post with 🚀😎🤯 8 days ago

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2104

The deepseek-ai/DeepSeek-V3-Base
model has featured today on CNBC tech news. The whale made a splash by using FP8 and shrink the cost of training significantly!

https://youtu.be/NJljq429cGk?si=kgk-ogPTMfJKsaA2

3 replies

reacted to sequelbox's post with 👀👍 8 days ago

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2123

Check out the early preview of the upcoming Tachibana-QVQ dataset: code-reasoning and code-instruct data generated with Qwen/QVQ-72B-Preview

Link here: sequelbox/Tachibana-QVQ-PREVIEW

more to come :)

1 reply

reacted to merve's post with ❤️🚀🔥 8 days ago

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4713

supercharge your LLM apps with smolagents 🔥

however cool your LLM is, without being agentic it can only go so far

enter smolagents: a new agent library by Hugging Face to make the LLM write code, do analysis and automate boring stuff!

Here's our blog for you to get started https://huggingface.co/blog/smolagents

reacted to tomaarsen's post with 😎🔥❤️ 8 days ago

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That didn't take long! Nomic AI has finetuned the new ModernBERT-base encoder model into a strong embedding model for search, classification, clustering and more!

Details:
🤖 Based on ModernBERT-base with 149M parameters.
📊 Outperforms both nomic-embed-text-v1 and nomic-embed-text-v1.5 on MTEB!
🏎️ Immediate FA2 and unpacking support for super efficient inference.
🪆 Trained with Matryoshka support, i.e. 2 valid output dimensionalities: 768 and 256.
➡️ Maximum sequence length of 8192 tokens!
2️⃣ Trained in 2 stages: unsupervised contrastive data -> high quality labeled datasets.
➕ Integrated in Sentence Transformers, Transformers, LangChain, LlamaIndex, Haystack, etc.
🏛️ Apache 2.0 licensed: fully commercially permissible

Try it out here: nomic-ai/modernbert-embed-base

Very nice work by Zach Nussbaum and colleagues at Nomic AI.