Instructions to use mukel/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 mukel/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="mukel/Meta-Llama-3.1-8B-Instruct-GGUF",
	filename="Meta-Llama-3.1-8B-Instruct-Q4_0.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 mukel/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 mukel/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf mukel/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mukel/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf mukel/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_0

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 mukel/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf mukel/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_0

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 mukel/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mukel/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_0

Use Docker

docker model run hf.co/mukel/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_0

LM Studio
Jan
Ollama
How to use mukel/Meta-Llama-3.1-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/mukel/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_0
```

Unsloth Studio new

How to use mukel/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 mukel/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 mukel/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 mukel/Meta-Llama-3.1-8B-Instruct-GGUF to start chatting

Docker Model Runner
How to use mukel/Meta-Llama-3.1-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/mukel/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_0
```

Lemonade

How to use mukel/Meta-Llama-3.1-8B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mukel/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_0

Run and chat with the model

lemonade run user.Meta-Llama-3.1-8B-Instruct-GGUF-Q4_0

List all available models

lemonade list

GGUF models for llama3.java

Pure .gguf Q4_0 and Q8_0 quantizations of Llama 3 8B instruct, ready to consume by llama3.java.

In the wild, Q8_0 quantizations are fine, but Q4_0 quantizations are rarely pure e.g. the output.weights tensor is quantized with Q6_K, instead of Q4_0.
A pure Q4_0 quantization can be generated from a high precision (F32, F16, BFLOAT16) .gguf source with the quantize utility from llama.cpp as follows:

./quantize --pure ./Meta-Llama-3-8B-Instruct-F32.gguf ./Meta-Llama-3-8B-Instruct-Q4_0.gguf Q4_0

Meta-Llama-3.1-8B-Instruct-GGUF

This is GGUF quantized version of meta-llama/Meta-Llama-3.1-8B-Instruct created using llama.cpp

Downloads last month: 22

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Collection including mukel/Meta-Llama-3.1-8B-Instruct-GGUF

Llama 3+

Collection

Meta's open Llama models • 4 items • Updated Oct 10, 2024