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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf liashchynskyi/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf liashchynskyi/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 serve -hf liashchynskyi/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf liashchynskyi/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 liashchynskyi/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf liashchynskyi/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 liashchynskyi/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf liashchynskyi/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use liashchynskyi/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 "liashchynskyi/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": "liashchynskyi/Meta-Llama-3-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull liashchynskyi/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

Atomic Chat

Description

This repository contains GGUF format model files for Meta LLama 3 Instruct.

Prompt template

<|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

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

Same as here: https://ollama.com/library/llama3:instruct/blobs/8ab4849b038c

Downloading using huggingface-cli

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you need:

huggingface-cli download liashchynskyi/Meta-Llama-3-8B-Instruct-GGUF --include "meta-llama-3-8b-instruct.Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False

Downloads last month: 82

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

Model tree for liashchynskyi/Meta-Llama-3-8B-Instruct-GGUF

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meta-llama/Meta-Llama-3-8B-Instruct

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