Tyler Williams's picture

Building on HF

Tyler Williams PRO

unmodeled-tyler

·

https://quantaintellect.com

AI & ML interests

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

Recent Activity

updated a collection 8 days ago

My Current Open Source Daily Drivers

repliedto RakshitAralimatti's post 9 days ago

🔥 GLM-5.1 (zai-org/GLM-5.1) — 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 ❤️ 9 days ago

📣 I just published a free course on Reinforcement Learning Environments for Language Models! 📌 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 🔹 Agents, Environments, and LLMs: how to map Reinforcement Learning concepts to the LLM domain 🔹 How to use Verifiers (open-source library by Prime Intellect) to build RL environments as software artifacts 🔹 Common patterns: How to build single-turn, multi-turn, and tool-use environments 🔹 Hands-on: turn a small language model (LFM2-2.6B by LiquidAI) into a Tic Tac Toe master 🔸 Build the game Environment 🔸 Use it to generate synthetic data for SFT warm-up 🔸 Group-based Reinforcement Learning If you're interested in building "little worlds" where LLMs can learn, this course is for you. --- 🤗🕹️ Play against the trained model: https://huggingface.co/spaces/anakin87/LFM2-2.6B-mr-tictactoe 📚 HF collection (datasets + models): https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe

View all activity

Organizations

unmodeled-tyler 's collections 3