Instructions to use lschaffer/qwen2_5_1_5b-git with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use lschaffer/qwen2_5_1_5b-git with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="lschaffer/qwen2_5_1_5b-git", filename="qwen25-1p5b-git-q5_k_m.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use lschaffer/qwen2_5_1_5b-git with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf lschaffer/qwen2_5_1_5b-git:Q5_K_M # Run inference directly in the terminal: llama cli -hf lschaffer/qwen2_5_1_5b-git:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf lschaffer/qwen2_5_1_5b-git:Q5_K_M # Run inference directly in the terminal: llama cli -hf lschaffer/qwen2_5_1_5b-git:Q5_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 lschaffer/qwen2_5_1_5b-git:Q5_K_M # Run inference directly in the terminal: ./llama-cli -hf lschaffer/qwen2_5_1_5b-git:Q5_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 lschaffer/qwen2_5_1_5b-git:Q5_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf lschaffer/qwen2_5_1_5b-git:Q5_K_M
Use Docker
docker model run hf.co/lschaffer/qwen2_5_1_5b-git:Q5_K_M
- LM Studio
- Jan
- vLLM
How to use lschaffer/qwen2_5_1_5b-git with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lschaffer/qwen2_5_1_5b-git" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lschaffer/qwen2_5_1_5b-git", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/lschaffer/qwen2_5_1_5b-git:Q5_K_M
- Ollama
How to use lschaffer/qwen2_5_1_5b-git with Ollama:
ollama run hf.co/lschaffer/qwen2_5_1_5b-git:Q5_K_M
- Unsloth Studio
How to use lschaffer/qwen2_5_1_5b-git 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 lschaffer/qwen2_5_1_5b-git 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 lschaffer/qwen2_5_1_5b-git to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lschaffer/qwen2_5_1_5b-git to start chatting
- Pi
How to use lschaffer/qwen2_5_1_5b-git with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf lschaffer/qwen2_5_1_5b-git:Q5_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": "lschaffer/qwen2_5_1_5b-git:Q5_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use lschaffer/qwen2_5_1_5b-git with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf lschaffer/qwen2_5_1_5b-git:Q5_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 lschaffer/qwen2_5_1_5b-git:Q5_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use lschaffer/qwen2_5_1_5b-git with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf lschaffer/qwen2_5_1_5b-git:Q5_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "lschaffer/qwen2_5_1_5b-git:Q5_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use lschaffer/qwen2_5_1_5b-git with Docker Model Runner:
docker model run hf.co/lschaffer/qwen2_5_1_5b-git:Q5_K_M
- Lemonade
How to use lschaffer/qwen2_5_1_5b-git with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull lschaffer/qwen2_5_1_5b-git:Q5_K_M
Run and chat with the model
lemonade run user.qwen2_5_1_5b-git-Q5_K_M
List all available models
lemonade list
| license: apache-2.0 | |
| language: | |
| - en | |
| pipeline_tag: text-generation | |
| tags: | |
| - gguf | |
| - ollama | |
| - tool-calling | |
| - mcp | |
| - lora | |
| base_model: | |
| - Qwen/Qwen2.5-1.5B-Instruct | |
| # qwen25-1p5b-git | |
| > ⚠️ **This model is trained for the git MCP server.** | |
| > It is optimised for structured MCP tool-call generation and is not intended | |
| > to be a general-purpose assistant. | |
| GGUF model fine-tuned for structured MCP tool-calling, ready for local inference via [Ollama](https://ollama.com). | |
| ## Model Details | |
| | Property | Value | | |
| |---|---| | |
| | MCP server | git | | |
| | Base model (training) | mlx-community/Qwen2.5-1.5B-Instruct-4bit | | |
| | Base model (fused export) | Qwen/Qwen2.5-1.5B-Instruct | | |
| | Fine-tune method | QLoRA / LoRA adapter fusion | | |
| | Quantization | Q5_K_M (5-bit, recommended) | | |
| | GGUF file | qwen25-1p5b-git-q5_k_m.gguf | | |
| | Preset | qwen2_5_1_5b | | |
| ## Intended Use | |
| **Intended for agents or apps that call the git MCP server.** | |
| The model was trained on a custom MCP tool-call JSONL dataset derived from the | |
| server's tool schema. It is intended to emit structured JSON tool calls for | |
| that server and is not suited for general chat. | |
| ## Quick Start (Ollama) | |
| ### Option 1: Direct from Ollama registry (if published) | |
| ```bash | |
| ollama pull qwen25-1p5b-git | |
| ollama run qwen25-1p5b-git | |
| ``` | |
| ### Option 2: Download from HuggingFace | |
| \`\`\`bash | |
| # 1. Download the GGUF and Modelfile to the same directory | |
| curl -L -o qwen25-1p5b-git-q5_k_m.gguf https://huggingface.co/lschaffer/qwen2_5_1_5b-git/resolve/main/qwen25-1p5b-git-q5_k_m.gguf | |
| curl -L -o Modelfile https://huggingface.co/lschaffer/qwen2_5_1_5b-git/resolve/main/Modelfile | |
| # 2. Register with Ollama (creates local model from GGUF + Modelfile) | |
| ollama create qwen25-1p5b-git -f Modelfile | |
| # 3. Run the model | |
| ollama run qwen25-1p5b-git | |
| \`\`\` | |
| **Important:** The \`Modelfile\` contains the required TEMPLATE directive for tool support. | |
| Registering the GGUF without the Modelfile will result in "does not support tools" errors. | |
| ## Optional Links | |
| - Docs: https://lschaffer.github.io/tealkit | |
| - Source: https://github.com/lschaffer/tealkit | |
| ## Files | |
| - `qwen25-1p5b-git-q5_k_m.gguf` | |
| - `Modelfile` — Ollama model definition with system prompt | |
| ## Notes | |
| Produced via LoRA fine-tuning on Mac Apple Silicon (MLX), adapter fusion, and llama.cpp GGUF conversion. | |
| See the [training guide](https://github.com/lschaffer/tealkit/blob/master_v2/traning/README.md) for full pipeline details. | |