Instructions to use MaziyarPanahi/firefunction-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/firefunction-v2-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MaziyarPanahi/firefunction-v2-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MaziyarPanahi/firefunction-v2-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MaziyarPanahi/firefunction-v2-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 MaziyarPanahi/firefunction-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MaziyarPanahi/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 serve -hf MaziyarPanahi/firefunction-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MaziyarPanahi/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 MaziyarPanahi/firefunction-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MaziyarPanahi/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 MaziyarPanahi/firefunction-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MaziyarPanahi/firefunction-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MaziyarPanahi/firefunction-v2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use MaziyarPanahi/firefunction-v2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/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": "MaziyarPanahi/firefunction-v2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/firefunction-v2-GGUF:Q4_K_M

SGLang

How to use MaziyarPanahi/firefunction-v2-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 "MaziyarPanahi/firefunction-v2-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": "MaziyarPanahi/firefunction-v2-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 "MaziyarPanahi/firefunction-v2-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": "MaziyarPanahi/firefunction-v2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use MaziyarPanahi/firefunction-v2-GGUF with Ollama:
```
ollama run hf.co/MaziyarPanahi/firefunction-v2-GGUF:Q4_K_M
```

Unsloth Studio

How to use MaziyarPanahi/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 MaziyarPanahi/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 MaziyarPanahi/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 MaziyarPanahi/firefunction-v2-GGUF to start chatting

How to use MaziyarPanahi/firefunction-v2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MaziyarPanahi/firefunction-v2-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "MaziyarPanahi/firefunction-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use MaziyarPanahi/firefunction-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/firefunction-v2-GGUF:Q4_K_M
```

Lemonade

How to use MaziyarPanahi/firefunction-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MaziyarPanahi/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

Hermes Agent

How to use MaziyarPanahi/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 serve -hf MaziyarPanahi/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 MaziyarPanahi/firefunction-v2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use MaziyarPanahi/firefunction-v2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MaziyarPanahi/firefunction-v2-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 "MaziyarPanahi/firefunction-v2-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"

firefunction-v2-GGUF / README.md

MaziyarPanahi

Update README.md (#1)

f382516 verified about 2 years ago

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	---
	tags:
	- quantized
	- 2-bit
	- 3-bit
	- 4-bit
	- 5-bit
	- 6-bit
	- 8-bit
	- GGUF
	- transformers
	- safetensors
	- text-generation
	- conversational
	- function-calling
	- text-generation-inference
	- region:us
	- text-generation
	model_name: MaziyarPanahi/firefunction-v2-GGUF
	base_model: fireworks-ai/firefunction-v2
	inference: false
	model_creator: fireworks-ai
	pipeline_tag: text-generation
	quantized_by: MaziyarPanahi
	license: llama3
	---
	# [MaziyarPanahi/firefunction-v2-GGUF](https://huggingface.co/MaziyarPanahi/firefunction-v2-GGUF)
	- Model creator: [fireworks-ai](https://huggingface.co/fireworks-ai)
	- Original model: [fireworks-ai/firefunction-v2](https://huggingface.co/fireworks-ai/firefunction-v2)

	## Description
	[MaziyarPanahi/firefunction-v2-GGUF](https://huggingface.co/MaziyarPanahi/firefunction-v2-GGUF) contains GGUF format model files for [fireworks-ai/firefunction-v2](https://huggingface.co/fireworks-ai/firefunction-v2).

	### About GGUF

	GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

	Here is an incomplete list of clients and libraries that are known to support GGUF:

	* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
	* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
	* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
	* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
	* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
	* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
	* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
	* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
	* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
	* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

	## Special thanks

	🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.

	Original README
	---
	# FireFunction V2: Fireworks Function Calling Model

	[Try on Fireworks](https://fireworks.ai/models/fireworks/firefunction-v2) \| [API Docs](https://readme.fireworks.ai/docs/function-calling) \| [Demo App](https://functional-chat.vercel.app/) \| [Discord](https://discord.gg/mMqQxvFD9A)

	<img src="https://cdn-uploads.huggingface.co/production/uploads/64b6f3a72f5a966b9722de88/nJNtxLzWswBDKK1iOZblb.png" alt="firefunction" width="400"/>

	FireFunction is a state-of-the-art function calling model with a commercially viable license. View detailed info in our [announcement blog](https://fireworks.ai/blog/firefunction-v2-launch-post). Key info and highlights:

	Comparison with other models:
	- Competitive with GPT-4o at function-calling, scoring 0.81 vs 0.80 on a medley of public evaluations
	- Trained on Llama 3 and retains Llama 3’s conversation and instruction-following capabilities, scoring 0.84 vs Llama 3’s 0.89 on MT bench
	- Significant quality improvements over FireFunction v1 across the broad range of metrics


	General info:

	🐾 Successor of the [FireFunction](https://fireworks.ai/models/fireworks/firefunction-v1) model

	🔆 Support of parallel function calling (unlike FireFunction v1) and good instruction following

	💡 Hosted on the [Fireworks](https://fireworks.ai/models/fireworks/firefunction-v2) platform at < 10% of the cost of GPT 4o and 2x the speed