Instructions to use GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-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 GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-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 GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-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 GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF:Q4_0

Use Docker

docker model run hf.co/GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-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": "GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF:Q4_0

Ollama
How to use GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF with Ollama:
```
ollama run hf.co/GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF:Q4_0
```

Unsloth Studio new

How to use GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-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 GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-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 GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF to start chatting

Docker Model Runner
How to use GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF with Docker Model Runner:
```
docker model run hf.co/GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF:Q4_0
```

Lemonade

How to use GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF:Q4_0

Run and chat with the model

lemonade run user.Meta-Llama-3.1-8B-Instruct-128k-GGUF-Q4_0

List all available models

lemonade list

This is a model that is assumed to perform well, but may require more testing and user feedback. Be aware, only models featured within the GUI of GPT4All, are curated and officially supported by Nomic. Use at your own risk.

About

Model converted and quantized by: 3Simplex.
GPT4All v3.1.1 required.

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: 1,761

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

16-bit

Model tree for GPT4All-Community/Meta-Llama-3.1-8B-Instruct-128k-GGUF

Base model

meta-llama/Llama-3.1-8B

Finetuned

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

Quantized

(634)

this model

Adapters

2 models