Instructions to use cuijian0819/gpt-oss-20b-function-calling-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use cuijian0819/gpt-oss-20b-function-calling-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 cuijian0819/gpt-oss-20b-function-calling-gguf:F16 # Run inference directly in the terminal: llama cli -hf cuijian0819/gpt-oss-20b-function-calling-gguf:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cuijian0819/gpt-oss-20b-function-calling-gguf:F16 # Run inference directly in the terminal: llama cli -hf cuijian0819/gpt-oss-20b-function-calling-gguf:F16
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 cuijian0819/gpt-oss-20b-function-calling-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf cuijian0819/gpt-oss-20b-function-calling-gguf:F16
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 cuijian0819/gpt-oss-20b-function-calling-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf cuijian0819/gpt-oss-20b-function-calling-gguf:F16
Use Docker
docker model run hf.co/cuijian0819/gpt-oss-20b-function-calling-gguf:F16
- LM Studio
- Jan
- vLLM
How to use cuijian0819/gpt-oss-20b-function-calling-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cuijian0819/gpt-oss-20b-function-calling-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": "cuijian0819/gpt-oss-20b-function-calling-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cuijian0819/gpt-oss-20b-function-calling-gguf:F16
- Ollama
How to use cuijian0819/gpt-oss-20b-function-calling-gguf with Ollama:
ollama run hf.co/cuijian0819/gpt-oss-20b-function-calling-gguf:F16
- Unsloth Studio
How to use cuijian0819/gpt-oss-20b-function-calling-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 cuijian0819/gpt-oss-20b-function-calling-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 cuijian0819/gpt-oss-20b-function-calling-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cuijian0819/gpt-oss-20b-function-calling-gguf to start chatting
- Pi
How to use cuijian0819/gpt-oss-20b-function-calling-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf cuijian0819/gpt-oss-20b-function-calling-gguf:F16
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": "cuijian0819/gpt-oss-20b-function-calling-gguf:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use cuijian0819/gpt-oss-20b-function-calling-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf cuijian0819/gpt-oss-20b-function-calling-gguf:F16
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 "cuijian0819/gpt-oss-20b-function-calling-gguf:F16" \ --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 cuijian0819/gpt-oss-20b-function-calling-gguf with Docker Model Runner:
docker model run hf.co/cuijian0819/gpt-oss-20b-function-calling-gguf:F16
- Lemonade
How to use cuijian0819/gpt-oss-20b-function-calling-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cuijian0819/gpt-oss-20b-function-calling-gguf:F16
Run and chat with the model
lemonade run user.gpt-oss-20b-function-calling-gguf-F16
List all available models
lemonade list
- Hermes Agent
How to use cuijian0819/gpt-oss-20b-function-calling-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 cuijian0819/gpt-oss-20b-function-calling-gguf:F16
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 cuijian0819/gpt-oss-20b-function-calling-gguf:F16
Run Hermes
hermes
- Atomic Chat
GPT-OSS-20B Function Calling GGUF
This repository contains the GPT-OSS-20B model fine-tuned on function calling data, converted to GGUF format for efficient inference with llama.cpp and Ollama.
Model Details
- Base Model: openai/gpt-oss-20b
- Fine-tuning Dataset: Salesforce/xlam-function-calling-60k (2000 samples)
- Fine-tuning Method: LoRA (r=8, alpha=16)
- Context Length: 131,072 tokens
- Model Size: 20B parameters
Files
gpt-oss-20b-function-calling-f16.gguf: F16 precision model (best quality)gpt-oss-20b-function-calling.Q4_K_M.gguf: Q4_K_M quantized model (recommended for inference)
Usage
With Ollama (Recommended)
# Direct from Hugging Face
ollama run hf.co/cuijian0819/gpt-oss-20b-function-calling-gguf:Q4_K_M
# Or create local model
ollama create my-gpt-oss -f Modelfile
ollama run my-gpt-oss
With llama.cpp
# Download model
wget https://huggingface.co/cuijian0819/gpt-oss-20b-function-calling-gguf/resolve/main/gpt-oss-20b-function-calling.Q4_K_M.gguf
# Run inference
./llama-cli -m gpt-oss-20b-function-calling.Q4_K_M.gguf -p "Your prompt here"
Example Modelfile for Ollama
FROM ./gpt-oss-20b-function-calling.Q4_K_M.gguf
TEMPLATE """<|start|>user<|message|>{{ .Prompt }}<|end|>
<|start|>assistant<|channel|>final<|message|>"""
PARAMETER temperature 0.7
PARAMETER top_p 0.9
SYSTEM """You are a helpful AI assistant that can call functions to help users."""
PyTorch Version
For training and fine-tuning with PyTorch/Transformers, check out the PyTorch version: cuijian0819/gpt-oss-20b-function-calling
Performance
The Q4_K_M quantized version provides excellent performance:
- Size Reduction: ~62% smaller than F16
- Memory Requirements: ~16GB VRAM recommended
- Quality: Minimal degradation from quantization
License
This model inherits the license from the base openai/gpt-oss-20b model.
Citation
@misc{gpt-oss-20b-function-calling-gguf,
title={GPT-OSS-20B Function Calling GGUF},
author={cuijian0819},
year={2025},
url={https://huggingface.co/cuijian0819/gpt-oss-20b-function-calling-gguf}
}
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Base model
openai/gpt-oss-20b