How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Sweaterdog/Smol-Reason:
# Run inference directly in the terminal:
llama-cli -hf Sweaterdog/Smol-Reason:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Sweaterdog/Smol-Reason:
# Run inference directly in the terminal:
llama-cli -hf Sweaterdog/Smol-Reason:
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Sweaterdog/Smol-Reason:
# Run inference directly in the terminal:
./llama-cli -hf Sweaterdog/Smol-Reason:
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Sweaterdog/Smol-Reason:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Sweaterdog/Smol-Reason:
Use Docker
docker model run hf.co/Sweaterdog/Smol-Reason:
Quick Links
A newer version of this model is available: Sweaterdog/Smol-reason2.1-LoRA

🧠 Smol-reason, a 3B model test for future models 🧠

Why?

When making the Andy series of models, I have been using PPO techniques to train models.

But as the bleeding edge of small models is becoming clear, reasoning models are the winners.

So, in order to learn the nuances of training models, I decided to train a small 3B model using GRPO techniques instead of PPO.

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The base model was Qwen2.5 3B, it is very smart as is, and even smarter with reasoning.

This model uses the following format while responding:

<think>
--reasoning content here--
</think>
<answer
--answer content here--
</answer>

Similar to the XML reasoning format but changed to use DeepSeek-R1 / QwQ thinking blocks.

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Model size
3B params
Architecture
qwen2
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Collection including Sweaterdog/Smol-Reason