Text Generation
MLX
Safetensors
PyTorch
English
llama4_text
facebook
meta
mobilellm
conversational
4-bit precision
How to use from
PiConfigure 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": "samairtimer/MobileLLM-R1-360M-4bit"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
piQuick Links
samairtimer/MobileLLM-R1-360M-4bit
This model samairtimer/MobileLLM-R1-360M-4bit was converted to MLX format from facebook/MobileLLM-R1-360M using mlx-lm version 0.27.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("samairtimer/MobileLLM-R1-360M-4bit")
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)
- Downloads last month
- 9
Model size
50M params
Tensor type
F32
·
U32 ·
Hardware compatibility
Log In to add your hardware
4-bit
Model tree for samairtimer/MobileLLM-R1-360M-4bit
Base model
facebook/MobileLLM-R1-360M-base Finetuned
facebook/MobileLLM-R1-360M
Start the MLX server
# Install MLX LM: uv tool install mlx-lm# Start a local OpenAI-compatible server: mlx_lm.server --model "samairtimer/MobileLLM-R1-360M-4bit"