---
language:
- en

license: apache-2.0

base_model:
- Qwen/Qwen3-4B

pipeline_tag: text-generation

tags:
- qwen3
- computer-science
- software-engineering
- programming
- awq
- compressed-tensors
- int4
- w4a16
- code
- python
---

# 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

| Property | Value |
|-----------|-------|
| Base Model | Qwen/Qwen3-4B |
| Model Family | Qwen3-4B-Computer-Science |
| Quantization | AWQ |
| Storage Format | Compressed-Tensors |
| Weight Precision | INT4 |
| Activation Precision | FP16 / BF16 |
| Quantization Scheme | W4A16 |
| Group Size | 128 |
| Weight Quantization | Asymmetric |
| lm_head | Excluded from Quantization |
| Language | English |
| License | Apache-2.0 |

---

## Training Data

The base model was instruction tuned using permissively licensed datasets.

| Dataset | Configuration | License |
|---------|---------------|---------|
| HuggingFaceTB/smoltalk | smol-magpie-ultra | Apache-2.0 |
| agentica-org/DeepCoder-Preview-Dataset | primeintellect | MIT |

### Dataset Size

| Split | Samples |
|-------|---------:|
| Training | 60,989 |
| Evaluation | 512 |

---

## 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.

| Parameter | Value |
|-----------|-------|
| Weight Format | Packed INT4 |
| Group Size | 128 |
| Symmetric | No |
| Observer | memoryless_minmax |
| Compression Format | Compressed-Tensors |

---

## 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

```python
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:

```bash
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

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