Instructions to use bartowski/firefunction-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bartowski/firefunction-v2-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/firefunction-v2-GGUF", filename="firefunction-v2-IQ1_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use bartowski/firefunction-v2-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/firefunction-v2-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/firefunction-v2-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/firefunction-v2-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/firefunction-v2-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 bartowski/firefunction-v2-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/firefunction-v2-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 bartowski/firefunction-v2-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/firefunction-v2-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/firefunction-v2-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/firefunction-v2-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/firefunction-v2-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": "bartowski/firefunction-v2-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bartowski/firefunction-v2-GGUF:Q4_K_M
- Ollama
How to use bartowski/firefunction-v2-GGUF with Ollama:
ollama run hf.co/bartowski/firefunction-v2-GGUF:Q4_K_M
- Unsloth Studio new
How to use bartowski/firefunction-v2-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 bartowski/firefunction-v2-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 bartowski/firefunction-v2-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bartowski/firefunction-v2-GGUF to start chatting
- Pi new
How to use bartowski/firefunction-v2-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bartowski/firefunction-v2-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": "bartowski/firefunction-v2-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bartowski/firefunction-v2-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf bartowski/firefunction-v2-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 bartowski/firefunction-v2-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use bartowski/firefunction-v2-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/firefunction-v2-GGUF:Q4_K_M
- Lemonade
How to use bartowski/firefunction-v2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/firefunction-v2-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.firefunction-v2-GGUF-Q4_K_M
List all available models
lemonade list
Update README.md
Browse files
README.md
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## Prompt format
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```
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## Download a file (not the whole branch) from below:
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## Prompt format
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```
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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You are a helpful assistant with access to functions.
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In addition to plain text responses, you can chose to call one or more of the provided functions.
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Use the following rule to decide when to call a function:
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* if the response can be generated from your internal knowledge (e.g., as in the case of queries like "What is the capital of Poland?"), do so
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* if you need external information that can be obtained by calling one or more of the provided functions, generate a function calls
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If you decide to call functions:
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* prefix function calls with functools marker (no closing marker required)
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* all function calls should be generated in a single JSON list formatted as functools[{"name": [function name], "arguments": [function arguments as JSON]},...]
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* follow the provided JSON schema. Do not hallucinate arguments or values. Do to blindly copy values from the provided samples
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* respect the argument type formatting. E.g., if the type if number and format is float, write value 7 as 7.0
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* make sure you pick the right functions that match the user intent
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Available functions as JSON spec:
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[
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{functions}
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]
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Today is {datetime}.<|eot_id|><|start_header_id|>user<|end_header_id|>
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{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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```
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## Download a file (not the whole branch) from below:
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