Qwen/Qwen3.8-27B
Image-Text-to-Text • 28B • Updated • 268k • • 10.2k
Fabio Ferrua
Marvin73
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Qwen3.6-27B-pi-tune v2 is coming plus its sibling 35B-A3B variant
I just wanted to share an update on the progress of future releases for Qwen3.6-pi-tuned family models.
Both 27B and 35B models are now unified under native think/no-think functionality.
The biggest lesson I started to learn after reviewing many suggestions: Fine-tuning for local open-weight agents isn't just about raw coding capability or benchmark numbers. Harness fluency, tool-calling, validation loops, and user-facing behavior matter just as much, sometimes more.
That insight and philosophy is exactly what v2 is based on.
Although it hasn't even been a month since the original release I wanted to get out the 35B-A3B variant as soon as possible due to popular demand.
With the current and upcoming releases of a new class of Agentic LLM's (Fable, GPT5.6, etc) expect v3 to be the best yet.
For everyone already running the original: what's it doing well, and what makes you reach for a different model? These suggestions help shape future releases.
https://huggingface.co/collections/bytkim/qwen36-pi-tune
PS: Heres a sneak peek at the newest version with the new 35B-A3B variant performing a one shot task. 😁
I just wanted to share an update on the progress of future releases for Qwen3.6-pi-tuned family models.
Both 27B and 35B models are now unified under native think/no-think functionality.
The biggest lesson I started to learn after reviewing many suggestions: Fine-tuning for local open-weight agents isn't just about raw coding capability or benchmark numbers. Harness fluency, tool-calling, validation loops, and user-facing behavior matter just as much, sometimes more.
That insight and philosophy is exactly what v2 is based on.
Although it hasn't even been a month since the original release I wanted to get out the 35B-A3B variant as soon as possible due to popular demand.
With the current and upcoming releases of a new class of Agentic LLM's (Fable, GPT5.6, etc) expect v3 to be the best yet.
For everyone already running the original: what's it doing well, and what makes you reach for a different model? These suggestions help shape future releases.
https://huggingface.co/collections/bytkim/qwen36-pi-tune
PS: Heres a sneak peek at the newest version with the new 35B-A3B variant performing a one shot task. 😁
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reacted to anakin87's post with ❤️ 4 months ago
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📣 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: anakin87/LFM2-2.6B-mr-tictactoe
📚 HF collection (datasets + models): https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
📌 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: anakin87/LFM2-2.6B-mr-tictactoe
📚 HF collection (datasets + models): https://huggingface.co/collections/anakin87/lfm2-26b-mr-tic-tac-toe
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