Instructions to use unclecode/llama3-function-call-Q4_K_M_GGFU-240424 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unclecode/llama3-function-call-Q4_K_M_GGFU-240424 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("unclecode/llama3-function-call-Q4_K_M_GGFU-240424", dtype="auto")

llama-cpp-python

How to use unclecode/llama3-function-call-Q4_K_M_GGFU-240424 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unclecode/llama3-function-call-Q4_K_M_GGFU-240424",
	filename="llama3-function-call-Q4_K_M_GGFU-240424-unsloth.Q4_K_M.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 unclecode/llama3-function-call-Q4_K_M_GGFU-240424 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unclecode/llama3-function-call-Q4_K_M_GGFU-240424:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf unclecode/llama3-function-call-Q4_K_M_GGFU-240424:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unclecode/llama3-function-call-Q4_K_M_GGFU-240424:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf unclecode/llama3-function-call-Q4_K_M_GGFU-240424: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 unclecode/llama3-function-call-Q4_K_M_GGFU-240424:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf unclecode/llama3-function-call-Q4_K_M_GGFU-240424: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 unclecode/llama3-function-call-Q4_K_M_GGFU-240424:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unclecode/llama3-function-call-Q4_K_M_GGFU-240424:Q4_K_M

Use Docker

docker model run hf.co/unclecode/llama3-function-call-Q4_K_M_GGFU-240424:Q4_K_M

LM Studio
Jan
Ollama
How to use unclecode/llama3-function-call-Q4_K_M_GGFU-240424 with Ollama:
```
ollama run hf.co/unclecode/llama3-function-call-Q4_K_M_GGFU-240424:Q4_K_M
```

Unsloth Studio new

How to use unclecode/llama3-function-call-Q4_K_M_GGFU-240424 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 unclecode/llama3-function-call-Q4_K_M_GGFU-240424 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 unclecode/llama3-function-call-Q4_K_M_GGFU-240424 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unclecode/llama3-function-call-Q4_K_M_GGFU-240424 to start chatting

Docker Model Runner
How to use unclecode/llama3-function-call-Q4_K_M_GGFU-240424 with Docker Model Runner:
```
docker model run hf.co/unclecode/llama3-function-call-Q4_K_M_GGFU-240424:Q4_K_M
```

Lemonade

How to use unclecode/llama3-function-call-Q4_K_M_GGFU-240424 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unclecode/llama3-function-call-Q4_K_M_GGFU-240424:Q4_K_M

Run and chat with the model

lemonade run user.llama3-function-call-Q4_K_M_GGFU-240424-Q4_K_M

List all available models

lemonade list

Function Calling and Tool Use LLaMA Models

This repository contains two main versions of LLaMA models fine-tuned for function calling and tool use capabilities:

Fine-tuned version of the LLama3-8b-instruct model
tinyllama - a smaller model version

For each version, the following variants are available:

16-bit quantized model
4-bit quantized model
GGFU format for use with llama.cpp

Dataset

The models were fine-tuned using a modified version of the ilacai/glaive-function-calling-v2-sharegpt dataset, which can be found at unclecode/glaive-function-calling-llama3.

Usage

To learn how to use these models, refer to the Colab notebook:

This is the first version of the models, and work is in progress to further train them with multi-tool detection and native tool binding support.

Library and Tools Support

A library is being developed to manage tools and add tool support for major LLMs, regardless of their built-in capabilities. You can find examples and contribute to the library at the following repository:

https://github.com/unclecode/fllm

Please open an issue in the repository for any bugs or collaboration requests.

Other Models

Here are links to other related models:

License

These models are released under the Apache 2.0 license.

Uploaded model

Developed by: unclecode
License: apache-2.0
Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 140

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for unclecode/llama3-function-call-Q4_K_M_GGFU-240424

Base model

unsloth/llama-3-8b-Instruct-bnb-4bit

Quantized

(662)

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