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Isaac Kargar

kargarisaac

https://kargarisaac.github.io/blog/

AI & ML interests

Interested in computer vision, nlp, and reinforcement learning

Recent Activity

upvoted an article 2 days ago

SmolVLA: Efficient Vision-Language-Action Model trained on Lerobot Community Data

upvoted an article 3 months ago

Open R1: How to use OlympicCoder locally for coding?

upvoted an article 3 months ago

SmolLM - blazingly fast and remarkably powerful

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kargarisaac's activity

upvoted an article 2 days ago

Article

SmolVLA: Efficient Vision-Language-Action Model trained on Lerobot Community Data

and 8 others •

10 days ago

• 124

upvoted 3 articles 3 months ago

Article

Open R1: How to use OlympicCoder locally for coding?

and 4 others •

Mar 20

• 60

Article

SmolLM - blazingly fast and remarkably powerful

and 2 others •

Jul 16, 2024

• 377

Article

Welcome Gemma 3: Google's all new multimodal, multilingual, long context open LLM

and 3 others •

Mar 12

• 432

upvoted a collection 3 months ago

GraphRAG Papers

Collection

Research relating graphs and GenAI. For discussion, find dedicated threads on https://discord.gg/graphrag • 48 items • Updated 6 days ago • 35

upvoted an article 3 months ago

Article

Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth

•

Jul 29, 2024

• 331

reacted to burtenshaw's post with 🚀 4 months ago

Post

7528

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.