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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upvoted an article about 23 hours ago

Open R1: Update #3

liked a model 8 days ago

Qwen/QwQ-32B

upvoted a collection 11 days ago

Tools for learning AI

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richardchai's activity

upvoted an article about 23 hours ago

Article

Open R1: Update #3

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1 day ago

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liked a model 8 days ago

Qwen/QwQ-32B

Text Generation • Updated 2 days ago • 256k • • 2.08k

upvoted a collection 11 days ago

Tools for learning AI

Collection

This is a collection of tools on the hub that teachers and students can use to learn AI! • 9 items • Updated 15 days ago • 66

liked 2 models 12 days ago

answerdotai/ModernBERT-large

Fill-Mask • Updated Jan 15 • 158k • 360

answerdotai/ModernBERT-base

Fill-Mask • Updated Jan 15 • 3.44M • 790

upvoted an article 12 days ago

Article

Finally, a Replacement for BERT: Introducing ModernBERT

Dec 19, 2024

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upvoted an article 13 days ago

Article

Fine-tune SmolLM's on custom synthetic data

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Jan 5

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HuggingFaceTB/finemath

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HuggingFaceTB/smoltalk

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upvoted an article 13 days ago

Article

Inside the family of Smol models

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14 days ago

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richardchai/SmolLM2-FT-MyDataset

Text Generation • Updated 14 days ago • 140

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HuggingFaceTB/SmolVLM-Instruct

Image-Text-to-Text • Updated 7 days ago • 66.5k • 408

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The Essential AI Toolkit

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A curated collection of AI tools for journalists & creators

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HuggingFaceTB/SmolLM2-1.7B-Instruct

Text Generation • Updated 7 days ago • 604k • • 573

published a model 18 days ago

richardchai/SmolLM2-FT-MyDataset

Text Generation • Updated 14 days ago • 140

upvoted a paper 19 days ago

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

Paper • 2502.02737 • Published Feb 4 • 203

liked a Space 21 days ago

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The Ultra-Scale Playbook

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The ultimate guide to training LLM on large GPU Clusters

reacted to burtenshaw's post with 🔥🤝❤️ 22 days ago

Post

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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.

2 replies