Instructions to use Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF 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 Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF: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 Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF: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 Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_K_M
- SGLang
How to use Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF with Ollama:
ollama run hf.co/Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_K_M
- Unsloth Studio
How to use Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF 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 Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF 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 Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF to start chatting
- Pi
How to use Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF: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": "Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_K_M
- Lemonade
How to use Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen2.5-Coder-1.5B-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF: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 Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_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 "Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF:Q4_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"
Update README.md
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README.md
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## Introduction
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Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen).
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- Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc.
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- A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.
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- **Long-context Support** up to 128K tokens.
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**This repo contains the instruction-tuned 1.5B Qwen2.5-Coder model in the GGUF FOrmat**, which has the following features:
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- Type: Causal Language Models
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## Introduction
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Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers; Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:
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- Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o.
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- A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.
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**This repo contains the instruction-tuned 1.5B Qwen2.5-Coder model in the GGUF FOrmat**, which has the following features:
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- Type: Causal Language Models
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