MLX How to use sykuang/Llama-3.2-Taiwan-3B-Instruct-MLX-4bit-vchewing 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("sykuang/Llama-3.2-Taiwan-3B-Instruct-MLX-4bit-vchewing")
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) MLX LM How to use sykuang/Llama-3.2-Taiwan-3B-Instruct-MLX-4bit-vchewing with MLX LM:
Generate or start a chat session
# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "sykuang/Llama-3.2-Taiwan-3B-Instruct-MLX-4bit-vchewing"
Run an OpenAI-compatible server
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "sykuang/Llama-3.2-Taiwan-3B-Instruct-MLX-4bit-vchewing"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "sykuang/Llama-3.2-Taiwan-3B-Instruct-MLX-4bit-vchewing",
"messages": [
{"role": "user", "content": "Hello"}
]
}' 
