SmolLM3-3B-mlx-16bit

16-bit MLX conversion of HuggingFaceTB/SmolLM3-3B for Apple Silicon.

Converted by: SirSahOl Source model: HuggingFaceTB/SmolLM3-3B Framework: MLX by Apple Quantization: 16-bit Format: safetensors License: apache-2.0

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Quick Start

Installation

pip install mlx-lm

CLI Usage

# Chat interactively
mlx_lm.chat --model SirSahOl/SmolLM3-3B-chat-mlx-16bit

# Generate text
mlx_lm.generate --model SirSahOl/SmolLM3-3B-chat-mlx-16bit --prompt "Your prompt here"

Python Usage

from mlx_lm import load, generate

model, tokenizer = load("SirSahOl/SmolLM3-3B-chat-mlx-16bit")
response = generate(model, tokenizer, prompt="Your prompt here", max_tokens=256)
print(response)

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Performance Benchmarks

Benchmarks coming soon.

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Who Should Use This?

General guidance:

• Use 4-bit if you want to run this model alongside other applications
• Use 8-bit if you have the memory and want better quality
• Use 16-bit for research, evaluation, or if memory isn't a concern

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Other Quantization Variants

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Conversion Details

Reproduction

To reproduce this conversion:

pip install mlx-lm==0.31.3
python3 -m mlx_lm.convert --hf-path HuggingFaceTB/SmolLM3-3B --mlx-path output/SmolLM3-3B-mlx-16bit

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Limitations & Known Issues

• Performance may degrade with very long contexts (>8K tokens) at lower quantization levels.
• This is a weight-only conversion; the model architecture and behavior are inherited from the source model.
• Quantization introduces a small quality loss compared to the original model. Lower bit counts = more loss.
• This model requires Apple Silicon (M1 or later) to run with MLX.

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License

This model conversion inherits the license of the source model: apache-2.0.

See the original model card for full license details.

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Changelog

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Converted with MLX Foundry — a professional pipeline for converting models to Apple MLX format.