Instructions to use theprint/VanRossum-Qwen2.5-Coder-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use theprint/VanRossum-Qwen2.5-Coder-3B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("theprint/VanRossum-Qwen2.5-Coder-3B", dtype="auto") - llama-cpp-python
How to use theprint/VanRossum-Qwen2.5-Coder-3B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="theprint/VanRossum-Qwen2.5-Coder-3B", filename="VanRossum-Qwen2.5-Coder-3B.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 theprint/VanRossum-Qwen2.5-Coder-3B with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf theprint/VanRossum-Qwen2.5-Coder-3B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf theprint/VanRossum-Qwen2.5-Coder-3B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf theprint/VanRossum-Qwen2.5-Coder-3B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf theprint/VanRossum-Qwen2.5-Coder-3B: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 theprint/VanRossum-Qwen2.5-Coder-3B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf theprint/VanRossum-Qwen2.5-Coder-3B: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 theprint/VanRossum-Qwen2.5-Coder-3B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf theprint/VanRossum-Qwen2.5-Coder-3B:Q4_K_M
Use Docker
docker model run hf.co/theprint/VanRossum-Qwen2.5-Coder-3B:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use theprint/VanRossum-Qwen2.5-Coder-3B with Ollama:
ollama run hf.co/theprint/VanRossum-Qwen2.5-Coder-3B:Q4_K_M
- Unsloth Studio new
How to use theprint/VanRossum-Qwen2.5-Coder-3B 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 theprint/VanRossum-Qwen2.5-Coder-3B 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 theprint/VanRossum-Qwen2.5-Coder-3B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for theprint/VanRossum-Qwen2.5-Coder-3B to start chatting
- Pi new
How to use theprint/VanRossum-Qwen2.5-Coder-3B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf theprint/VanRossum-Qwen2.5-Coder-3B:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "theprint/VanRossum-Qwen2.5-Coder-3B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use theprint/VanRossum-Qwen2.5-Coder-3B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf theprint/VanRossum-Qwen2.5-Coder-3B:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default theprint/VanRossum-Qwen2.5-Coder-3B:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use theprint/VanRossum-Qwen2.5-Coder-3B with Docker Model Runner:
docker model run hf.co/theprint/VanRossum-Qwen2.5-Coder-3B:Q4_K_M
- Lemonade
How to use theprint/VanRossum-Qwen2.5-Coder-3B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull theprint/VanRossum-Qwen2.5-Coder-3B:Q4_K_M
Run and chat with the model
lemonade run user.VanRossum-Qwen2.5-Coder-3B-Q4_K_M
List all available models
lemonade list
Homage to Python
This model has been trained for 1 epoch on the VanRossum dataset.
The VanRossum dataset is all Python! I used DataMix to combine a handful of highly rated Python-centric datasets, to get a sampling of each and create something new.
This data set has 80,000 entries and is named after Guido Van Rossum, the man who invented Python back in 1991.
See the VanRossum Collection on HF for all things related to this dataset.
Alpaca / GPT
There are 2 versions of this dataset available on Huggingface.
Uploaded model
- Developed by: theprint
- License: apache-2.0
- Finetuned from model : unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit
This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.
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