Qwen3-4B-Computer-Science-MLX-4bit

Qwen3-4B-Computer-Science-MLX-4bit

Overview

Qwen3-4B-Computer-Science-MLX-4bit is a 4-bit MLX conversion of Qwen3-4B-Computer-Science for inference on Apple Silicon.

The model was converted from the original BF16 Safetensors release using mlx-lm. It is intended for local inference on supported Mac systems using the MLX framework.

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Model Information

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Training Data

The source model was instruction-tuned using permissively licensed datasets.

Dataset Size

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Intended Use

This model is intended for:

• Software engineering
• Programming assistance
• Code generation
• Debugging
• Code review
• Algorithm implementation
• Computer science education
• General technical reasoning

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Installation

Install MLX-LM:

python -m pip install mlx-lm

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Usage

Command Line

mlx_lm.generate \
  --model Irfanuruchi/Qwen3-4B-Computer-Science-MLX-4bit \
  --prompt "Write a Python function that returns the first n Fibonacci numbers." \
  --max-tokens 200

Python

from mlx_lm import generate, load

model, tokenizer = load(
    "Irfanuruchi/Qwen3-4B-Computer-Science-MLX-4bit"
)

response = generate(
    model,
    tokenizer,
    prompt="Write a Python function that returns the first n Fibonacci numbers.",
    max_tokens=200,
)

print(response)

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Conversion

The model was converted from:

Irfanuruchi/Qwen3-4B-Computer-Science

Conversion configuration:

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Release Validation

The release was validated locally on Apple Silicon.

Performance measurements are from one local generation test and may vary by device, prompt, context length, and software version.

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Release Artifacts

model.safetensors
model.safetensors.index.json
config.json
generation_config.json
tokenizer.json
tokenizer_config.json
added_tokens.json
special_tokens_map.json
merges.txt
vocab.json
README.conversion.md
SHA256SUMS
LICENSE
README.md

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Integrity Verification

Verify the downloaded files on macOS:

shasum -a 256 -c SHA256SUMS

On Linux:

sha256sum -c SHA256SUMS

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Limitations

• Quantization can affect output quality relative to the BF16 checkpoint.
• The model may produce incorrect or incomplete code.
• Generated code should be reviewed and tested before use.
• Performance depends on the Apple Silicon device and available memory.
• This release is intended for MLX-compatible systems.

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License

The model is distributed under the Apache License 2.0.

The source model is based on Qwen3-4B, which is also distributed under the Apache License 2.0.

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Acknowledgements

• Alibaba Qwen Team
• Apple MLX Team
• Hugging Face
• SmolTalk contributors
• DeepCoder contributors

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Citation

@software{uruci2026qwen3computersciencemlx,
  title={Qwen3-4B-Computer-Science-MLX-4bit},
  author={Irfan Uruçi},
  year={2026},
  publisher={Hugging Face}
}