Instructions to use QuantFactory/Meta-Llama-3-8B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Meta-Llama-3-8B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Meta-Llama-3-8B-Instruct-GGUF",
	filename="Meta-Llama-3-8B-Instruct.Q2_K.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 QuantFactory/Meta-Llama-3-8B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Meta-Llama-3-8B-Instruct-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 QuantFactory/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Meta-Llama-3-8B-Instruct-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 QuantFactory/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Meta-Llama-3-8B-Instruct-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 QuantFactory/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Meta-Llama-3-8B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Meta-Llama-3-8B-Instruct-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": "QuantFactory/Meta-Llama-3-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Meta-Llama-3-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Meta-Llama-3-8B-Instruct-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 QuantFactory/Meta-Llama-3-8B-Instruct-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 QuantFactory/Meta-Llama-3-8B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Meta-Llama-3-8B-Instruct-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Meta-Llama-3-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Meta-Llama-3-8B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Meta-Llama-3-8B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

perfect upload. stop conversation when finished.

by robert1968 - opened Apr 19, 2024

Discussion

robert1968

Apr 19, 2024

This updated files works fine.
I use Meta-Llama-3-8B-Instruct.Q8_0.gguf and Meta-Llama-3-8B-Instruct.Q6_K.gguf and both perfectly stop conversation when finished.
Many thanks. :)

MoonRide

Apr 21, 2024

@0-hero Could you tell how did you make current GGUFs? They work well, and models stops their turn as they should - but when I tried to reproduce the conversion with convert-hf-to-gguf.py from current llama.cpp (b2709) that (in theory) supports Llama 3, I get GGUFs that just don't stop the generation. Did you change any of the tokenizer configuration files vs original Llama repo, and/or used any specific llama.cpp commit / PR?

0-hero

Quant Factory org Apr 21, 2024

@MoonRide we changed the config files from the original repo

MoonRide

Apr 21, 2024

@0-hero Could you share those changes, and/or maybe propose a PR to the original Meta repo?

0-hero

Quant Factory org Apr 21, 2024

I think the changes are already merged

MoonRide

Apr 21, 2024

@0-hero I pulled the current configs from original repo, but GGUFs made with llama.cpp b2709 still didn't stop the generation. But I experimented a bit, and changed
"eos_token": "<|end_of_text|>",
to
"eos_token": "<|eot_id|>",
in tokenizer_config.json, and that finally made the generation stop in Ollama after model turn (like in those GGUFs of yours). But I am not sure if that's the best / proper way, or if it won't have some side-effects in other apps. Always something with tokenizer and/or the chat template, sigh...

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