Text Generation
MLX
Safetensors
English
llama
alignment-handbook
trl
sft
apple-silicon
on-device
tiny-llm
smollm
quantized
conversational
4-bit precision
How to use from
MLX LMRun an OpenAI-compatible server
# Install MLX LM
uv tool install mlx-lm# Start the server
mlx_lm.server --model "Irfanuruchi/SmolLM-1.7B-Instruct-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Irfanuruchi/SmolLM-1.7B-Instruct-MLX-4bit",
"messages": [
{"role": "user", "content": "Hello"}
]
}'Quick Links
SmolLM-1.7B-Instruct (MLX 4-bit)
A 4-bit MLX quantized build of HuggingFaceTB/SmolLM-1.7B-Instruct, optimized for Apple Silicon local inference.
Benchmark Environment
- Device: MacBook Pro (M3 Pro)
- Runtime: MLX
- Quantization: ~4.5 bits per weight
Performance (Measured)
- Disk size: ~922 MB
- Peak memory: ~1.08 GB
- Generation speed: ~110 tokens/sec
Benchmarks were collected on macOS (M3 Pro).
Performance on iPhone / iPad will vary based on hardware and available memory.
Usage
mlx_lm.generate \
--model Irfanuruchi/SmolLM-1.7B-Instruct-MLX-4bit \
--prompt "In 5 sentences, explain the Pomodoro technique and how to start today." \
--max-tokens 140
License
Upstream SmolLM is released under Apache-2.0. Preserve attribution and the original license terms.
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Model size
0.3B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
Log In to add your hardware
4-bit
Model tree for Irfanuruchi/SmolLM-1.7B-Instruct-MLX-4bit
Base model
HuggingFaceTB/SmolLM-1.7B Quantized
HuggingFaceTB/SmolLM-1.7B-Instruct
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm# Interactive chat REPL mlx_lm.chat --model "Irfanuruchi/SmolLM-1.7B-Instruct-MLX-4bit"