Instructions to use meetkai/functionary-small-v2.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meetkai/functionary-small-v2.2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="meetkai/functionary-small-v2.2-GGUF",
	filename="functionary-small-v2.2.f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use meetkai/functionary-small-v2.2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf meetkai/functionary-small-v2.2-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf meetkai/functionary-small-v2.2-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf meetkai/functionary-small-v2.2-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf meetkai/functionary-small-v2.2-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 meetkai/functionary-small-v2.2-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf meetkai/functionary-small-v2.2-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 meetkai/functionary-small-v2.2-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf meetkai/functionary-small-v2.2-GGUF:F16

Use Docker

docker model run hf.co/meetkai/functionary-small-v2.2-GGUF:F16

LM Studio
Jan
Ollama
How to use meetkai/functionary-small-v2.2-GGUF with Ollama:
```
ollama run hf.co/meetkai/functionary-small-v2.2-GGUF:F16
```

Unsloth Studio new

How to use meetkai/functionary-small-v2.2-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 meetkai/functionary-small-v2.2-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 meetkai/functionary-small-v2.2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for meetkai/functionary-small-v2.2-GGUF to start chatting

Docker Model Runner
How to use meetkai/functionary-small-v2.2-GGUF with Docker Model Runner:
```
docker model run hf.co/meetkai/functionary-small-v2.2-GGUF:F16
```

Lemonade

How to use meetkai/functionary-small-v2.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull meetkai/functionary-small-v2.2-GGUF:F16

Run and chat with the model

lemonade run user.functionary-small-v2.2-GGUF-F16

List all available models

lemonade list

can this model be used with langchain llamacpp?

by Juanreatrepo77 - opened Feb 29, 2024

Discussion

Juanreatrepo77

Feb 29, 2024

First I want to thank you for your work and second, can this model be used with langchain llamacpp? When trying it I had problems due to tokenization

jeffreymeetkai

Feb 29, 2024

Hi, yes this model can be used with local OpenAI-compatible server with langchain. We have recently integrated functionary v2 into llama-cpp-python's OpenAI-compatible server in v0.2.50. You can find more details about setting up here and here in the Function Calling section.

The tokenization issue is due to llama.cpp's tokenizer unable to handle newly added tokens. We have fixed this issue in the llama-cpp-python integration by using the HF AutoTokenizer instead.

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