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 Settings

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

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

Atomic Chat new
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

Compatibility with llama-cpp and Ollama

#17

by liashchynskyi - opened May 25, 2024

Discussion

liashchynskyi

May 25, 2024

Hi there!

I've tried some quantized versions of this model and ran into an issue. I use llama-cpp-python for model inference. When I provide a question, I get infinite random characters as the result (see screenshot). But when I create a local model from the same quantized gguf by using Modelfile for Ollama inference, then everything works fine. So the issue is that Ollama works, and llama-cpp-python provides random output. The same behavior was noticed with a couple other models, like defog/llama-3-sqlcoder-8b.

Is anyone here experiencing same issues?

muzhig

Jun 2, 2024

llm_load_vocab:
llm_load_vocab: ************************************
llm_load_vocab: GENERATION QUALITY WILL BE DEGRADED!
llm_load_vocab: CONSIDER REGENERATING THE MODEL
llm_load_vocab: ************************************
llm_load_vocab:

jaycann2

Jun 6, 2024

•

edited Jun 6, 2024

Hi there!

I've tried some quantized versions of this model and ran into an issue. I use llama-cpp-python for model inference. When I provide a question, I get infinite random characters as the result (see screenshot). But when I create a local model from the same quantized gguf by using Modelfile for Ollama inference, then everything works fine. So the issue is that Ollama works, and llama-cpp-python provides random output. The same behavior was noticed with a couple other models, like defog/llama-3-sqlcoder-8b.

Is anyone here experiencing same issues?

@liashchynskyi Yes! I'm having the same issue with defog/llama-3-sqlcoder-8b. I'm using LangChain with llama-cpp-python - only GGUF models. I'm looking to use GGUF files others have created - I can look into generating my own if that's the only solution.

Output from defog/llama-3-sqlcoder-8b:

munish0838

Quant Factory org Jun 7, 2024

•

edited Jun 7, 2024

@jaycann2 can you try QuantFactory/Meta-Llama-3-8B-Instruct-GGUF-v2
I'll update the defog quants today if you are facing issues with them

liashchynskyi

Jun 7, 2024

•

edited Jun 7, 2024

@munish0838 But why do we receive random outputs? I've tried to quantize the original model myself and ran into the same issue.

jaycann2

Jun 7, 2024

@jaycann2 can you try QuantFactory/Meta-Llama-3-8B-Instruct-GGUF-v2
I'll update the defog quants today if you are facing issues with them

Thanks @munish0838 - I tried yesterday and got the same result. I'd be interested to see if you are able to duplicate the issue on you end, with the GGUF version. If not, I might be able to learn what's going on from your code.

munish0838

Quant Factory org Jun 8, 2024

@jaycann2 I updated the quants yesterday in this repo and defog-sql-llama repo, they are both working perfectly for me

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