Richard Chai PRO

richardchai

AI & ML interests

Natural Language, Computer Vision, Intelligent Agents https://www.linkedin.com/in/richardchai/ https://github.com/atsui888/

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liked a model about 3 hours ago

HuggingFaceTB/SmolLM2-1.7B-Instruct

updated a model about 12 hours ago

richardchai/SmolLM2-FT-MyDataset

published a model about 12 hours ago

richardchai/SmolLM2-FT-MyDataset

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richardchai's activity

liked a model about 3 hours ago

HuggingFaceTB/SmolLM2-1.7B-Instruct

Text Generation • Updated 18 days ago • 376k • • 554

updated a model about 12 hours ago

richardchai/SmolLM2-FT-MyDataset

Text Generation • Updated about 12 hours ago

published a model about 12 hours ago

richardchai/SmolLM2-FT-MyDataset

Text Generation • Updated about 12 hours ago

upvoted a paper 2 days ago

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model

Paper • 2502.02737 • Published 19 days ago • 190

liked a Space 4 days ago

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The Ultra-Scale Playbook

🌌

The ultimate guide to training LLM on large GPU Clusters

reacted to burtenshaw's post with 🔥🤝❤️🚀 5 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.