MLX How to use zecanard/gemma-4-31b-it-uncensored-heretic-ara-MLX-5bit-int5-affine with MLX:
# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config
# Load the model
model, processor = load("zecanard/gemma-4-31b-it-uncensored-heretic-ara-MLX-5bit-int5-affine")
config = load_config("zecanard/gemma-4-31b-it-uncensored-heretic-ara-MLX-5bit-int5-affine")
# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."
# Apply chat template
formatted_prompt = apply_chat_template(
processor, config, prompt, num_images=1
)
# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output) Pi new How to use zecanard/gemma-4-31b-it-uncensored-heretic-ara-MLX-5bit-int5-affine with Pi:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "zecanard/gemma-4-31b-it-uncensored-heretic-ara-MLX-5bit-int5-affine"
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": "zecanard/gemma-4-31b-it-uncensored-heretic-ara-MLX-5bit-int5-affine"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
