Instructions to use mlx-community/Meta-Llama-3.1-8B-Instruct-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Meta-Llama-3.1-8B-Instruct-4bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mlx-community/Meta-Llama-3.1-8B-Instruct-4bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Pi new
How to use mlx-community/Meta-Llama-3.1-8B-Instruct-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mlx-community/Meta-Llama-3.1-8B-Instruct-4bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default mlx-community/Meta-Llama-3.1-8B-Instruct-4bit
Run Hermes
hermes
- MLX LM
How to use mlx-community/Meta-Llama-3.1-8B-Instruct-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
I can not download the model
I want to download the model, but I keep encountering the following error message. How should I proceed? Thanks a lot for your help.
""" Model not found for path or HF repo: mlx-community/Meta-Llama-3.1-8B-Instruct-4bit.
Please make sure you specified the local path or Hugging Face repo id correctly.
If you are trying to access a private or gated Hugging Face repo, make sure you are authenticated:
https://huggingface.co/docs/huggingface_hub/en/guides/cli#huggingface-cli-login"""
it is my download model code.
"""
from mlx_lm import load, generate
from huggingface_hub import login
Step 1: Set Hugging Face Access Token
access_token = "hf_KCXXXX"
login(access_token)
Step 2: Attempt to load the model
try:
model_name = "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit" # Ensure the model name is correct
model, tokenizer = load(model_name)
print(f"Model {model_name} loaded successfully!")
# Step 3: Generate using the model
prompt = "hello"
response = generate(model, tokenizer, prompt=prompt, verbose=True)
print(f"Generation result: {response}")
except Exception as e:
print(f"An error occurred during model loading or generation: {e}")
""