Hugging Face's logo
Tyler Williams's picture
Building on HF
5 19 92

Tyler Williams PRO

unmodeled-tyler
Tico1982's profile picture hypothetical's profile picture POKO44's profile picture
Β·
https://quantaintellect.com

AI & ML interests

AI research engineer & solo operator of VANTA Research/Quanta Intellect

Recent Activity

updated a collection 5 days ago
My Current Open Source Daily Drivers
repliedto RakshitAralimatti's post 5 days ago
πŸ”₯ GLM-5.1 (zai-org/GLM-5.1) β€” Quietly One of the Best flagship model for agentic engineering and Coding tasks Right Now threw some LangGraph agent code at it, a messy RAG pipeline, some async Python stuff and it just handled it. no drama, no hallucinated methods, actually usable output on the first try. open source closing the gap this fast is genuinely exciting. go check zai-org/GLM-5.1 on HF if you haven't already Good work @zai-org-3
reacted to anakin87's post with ❀️ 5 days ago
πŸ“£ I just published a free course on Reinforcement Learning Environments for Language Models! πŸ“Œ COURSE: https://github.com/anakin87/llm-rl-environments-lil-course Over the past year, we've seen a shift in LLM Post-Training. Previously, Supervised Fine-Tuning was the most important part: making models imitate curated Question-Answer pairs. Now we also have Reinforcement Learning with Verifiable Rewards. With techniques like GRPO, models can learn through trial and error in dynamic environments. They can climb to new heights without relying on expensively prepared data. But what actually are these environments in practice❓ And how do you build them effectively❓ Fascinated by these concepts, I spent time exploring this space through experiments, post-training Small Language Models. I've packaged everything I learned into this short course. What you'll learn πŸ”Ή Agents, Environments, and LLMs: how to map Reinforcement Learning concepts to the LLM domain πŸ”Ή How to use Verifiers (open-source library by Prime Intellect) to build RL environments as software artifacts πŸ”Ή Common patterns: How to build single-turn, multi-turn, and tool-use environments πŸ”Ή Hands-on: turn a small language model (LFM2-2.6B by LiquidAI) into a Tic Tac Toe master πŸ”Έ Build the game Environment πŸ”Έ Use it to generate synthetic data for SFT warm-up πŸ”Έ Group-based Reinforcement Learning If you're interested in building "little worlds" where LLMs can learn, this course is for you. --- πŸ€—πŸ•ΉοΈ Play against the trained model: https://huggingface.co/spaces/anakin87/LFM2-2.6B-mr-tictactoe πŸ“š HF collection (datasets + models): https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
View all activity

Organizations

Blog-explorers's profile picture VANTA Research's profile picture Mistral Hack-a-ton 2026's profile picture Quanta Intellect's profile picture