Irfanuruchi/Qwen3-4B-Computer-Science-AWQ

Qwen3-4B-Computer-Science-AWQ

Overview

Qwen3-4B-Computer-Science-AWQ is the AWQ-calibrated quantized release of Qwen3-4B-Computer-Science.

Weights are stored using the Compressed-Tensors format with 4-bit asymmetric group-wise quantization (W4A16). The checkpoint was produced using Activation-aware Weight Quantization (AWQ) calibration and validated by successful quantization, checksum verification, and CPU inference.

Model Information

Training Data

The base model was instruction tuned using permissively licensed datasets.

Dataset Size

Intended Use

This model is intended for:

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

Quantization

This release was generated using Activation-aware Weight Quantization (AWQ).

The resulting checkpoint stores weights using packed 4-bit group-wise asymmetric quantization.

Runtime Compatibility

This checkpoint uses the Compressed-Tensors format.

It is intended for runtimes that support Compressed-Tensors models.

Validation performed for this release:

• —Successful AWQ calibration
• —Successful model serialization
• —CPU inference
• —SHA256 verification of release artifacts

Loading this checkpoint with standard Transformers may decompress weights during execution depending on the runtime and available hardware.

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Irfanuruchi/Qwen3-4B-Computer-Science-AWQ",
    device_map="auto",
    dtype="auto",
)

tokenizer = AutoTokenizer.from_pretrained(
    "Irfanuruchi/Qwen3-4B-Computer-Science-AWQ"
)

Release Artifacts

model.safetensors
config.json
generation_config.json
recipe.yaml
tokenizer.json
tokenizer_config.json
chat_template.jinja
SHA256SUMS
LICENSE
README.md

Integrity Verification

Every release artifact includes a SHA256 checksum.

Verify downloaded files:

sha256sum -c SHA256SUMS

Validation

The published checkpoint was verified before release.

Completed validation:

• —AWQ calibration completed successfully
• —Quantized checkpoint generated successfully
• —CPU inference completed successfully
• —SHA256 checksums verified

Limitations

• —Quantization may affect output quality compared to the BF16 checkpoint.
• —Runtime support depends on the inference engine.
• —GPU memory requirements depend on whether the runtime executes directly on compressed weights or decompresses them during inference.

License

Base model:

• —Apache-2.0

Training datasets:

• —Apache-2.0
• —MIT

This repository is distributed under the Apache-2.0 License.

Acknowledgements

• —Alibaba Qwen Team
• —Hugging Face
• —vLLM Project
• —LLM Compressor Project
• —SmolTalk Contributors
• —DeepCoder Contributors

Citation

@software{uruci2026qwen3csawq,
  title={Qwen3-4B-Computer-Science-AWQ},
  author={Irfan Uruçi},
  year={2026},
  publisher={Hugging Face}
}