Instructions to use Sweaterdog/Smol-Reason with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Sweaterdog/Smol-Reason with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Sweaterdog/Smol-Reason", filename="Smol-reason.F16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Sweaterdog/Smol-Reason with 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:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Sweaterdog/Smol-Reason:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Sweaterdog/Smol-Reason:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Sweaterdog/Smol-Reason:Q4_K_M
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:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Sweaterdog/Smol-Reason:Q4_K_M
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:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Sweaterdog/Smol-Reason:Q4_K_M
Use Docker
docker model run hf.co/Sweaterdog/Smol-Reason:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Sweaterdog/Smol-Reason with Ollama:
ollama run hf.co/Sweaterdog/Smol-Reason:Q4_K_M
- Unsloth Studio new
How to use Sweaterdog/Smol-Reason with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Sweaterdog/Smol-Reason to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Sweaterdog/Smol-Reason to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Sweaterdog/Smol-Reason to start chatting
- Docker Model Runner
How to use Sweaterdog/Smol-Reason with Docker Model Runner:
docker model run hf.co/Sweaterdog/Smol-Reason:Q4_K_M
- Lemonade
How to use Sweaterdog/Smol-Reason with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Sweaterdog/Smol-Reason:Q4_K_M
Run and chat with the model
lemonade run user.Smol-Reason-Q4_K_M
List all available models
lemonade list
🧠Smol-reason, a 3B model test for future models ðŸ§
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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docker model run hf.co/Sweaterdog/Smol-Reason: