s1-32B
Collection
s1-32B model - quantized and converted to MLX • 3 items • Updated
How to use from
PiConfigure the model in Pi
# Install Pi:
npm install -g @earendil-works/pi-coding-agent# Add to ~/.pi/agent/models.json:
{
"providers": {
"mlx-lm": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "moot20/s1-32B-MLX-6bits"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
piQuick Links
moot20/s1-32B-MLX-6bits
The Model moot20/s1-32B-MLX-6bits was converted to MLX format from simplescaling/s1-32B using mlx-lm version 0.21.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("moot20/s1-32B-MLX-6bits")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
33B params
Tensor type
U32
·
F16 ·
Hardware compatibility
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6-bit
Model tree for moot20/s1-32B-MLX-6bits
Base model
simplescaling/s1-32B
Start the MLX server
# Install MLX LM: uv tool install mlx-lm# Start a local OpenAI-compatible server: mlx_lm.server --model "moot20/s1-32B-MLX-6bits"