Instructions to use 3Simplex/Meta-Llama-3.1-8B-Instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 3Simplex/Meta-Llama-3.1-8B-Instruct-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="3Simplex/Meta-Llama-3.1-8B-Instruct-gguf",
	filename="Meta-Llama-3.1-8B-Instruct-128k-Q4_0.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 3Simplex/Meta-Llama-3.1-8B-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 3Simplex/Meta-Llama-3.1-8B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf 3Simplex/Meta-Llama-3.1-8B-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 3Simplex/Meta-Llama-3.1-8B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf 3Simplex/Meta-Llama-3.1-8B-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 3Simplex/Meta-Llama-3.1-8B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 3Simplex/Meta-Llama-3.1-8B-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 3Simplex/Meta-Llama-3.1-8B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 3Simplex/Meta-Llama-3.1-8B-Instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/3Simplex/Meta-Llama-3.1-8B-Instruct-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use 3Simplex/Meta-Llama-3.1-8B-Instruct-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "3Simplex/Meta-Llama-3.1-8B-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": "3Simplex/Meta-Llama-3.1-8B-Instruct-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/3Simplex/Meta-Llama-3.1-8B-Instruct-gguf:Q4_K_M

Ollama
How to use 3Simplex/Meta-Llama-3.1-8B-Instruct-gguf with Ollama:
```
ollama run hf.co/3Simplex/Meta-Llama-3.1-8B-Instruct-gguf:Q4_K_M
```

Unsloth Studio new

How to use 3Simplex/Meta-Llama-3.1-8B-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 3Simplex/Meta-Llama-3.1-8B-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 3Simplex/Meta-Llama-3.1-8B-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 3Simplex/Meta-Llama-3.1-8B-Instruct-gguf to start chatting

Docker Model Runner
How to use 3Simplex/Meta-Llama-3.1-8B-Instruct-gguf with Docker Model Runner:
```
docker model run hf.co/3Simplex/Meta-Llama-3.1-8B-Instruct-gguf:Q4_K_M
```

Lemonade

How to use 3Simplex/Meta-Llama-3.1-8B-Instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 3Simplex/Meta-Llama-3.1-8B-Instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Meta-Llama-3.1-8B-Instruct-gguf-Q4_K_M

List all available models

lemonade list

At the time of this release, llama.cpp did not support the rope scaling required for full context (limit is 8192). Soon this will be updated for full 128K functionality.
Depriciated models still listed do not have 128k mark.

The new release of llama.cpp and transformers have been applied and the gguf was tested.
Meta-Llama-3.1-8B-Instruct-128k
You will need to update llama.cpp and transformers to use the full context.

Prompt Template

<|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|>

<|start_header_id|>user<|end_header_id|>

{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{assistant_response}

128k Context Length

"llama.context_length": 131072

Downloads last month: 70

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

16-bit

Model tree for 3Simplex/Meta-Llama-3.1-8B-Instruct-gguf

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Quantized

(635)

this model