---
base_model: Qwen/Qwen3-0.6B
library_name: transformers
pipeline_tag: text-generation
tags:
- qwen3
- edgerazor
- quantization
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
---

<div align="center">
  <br/>
  <img src="./asset/Logo-HF.png" alt="EdgeRazor Logo" width="60%">
  <h3>
    EdgeRazor for Lightweight LLMs
  </h3>

  <p>
    <!-- <a href="https://arxiv.org/abs/2604.xxxxx" target="blank">
      <img src="https://img.shields.io/badge/arXiv-EdgeRazor-b31b1b?style=flat&logo=arxiv" alt="arXiv EdgeRazor">
    </a> -->
    <a href="https://github.com/zhangsq-nju/EdgeRazor" target="blank">
      <img src="https://img.shields.io/badge/GitHub-EdgeRazor-blue?style=flat&logo=github" alt="GitHub EdgeRazor">
    </a>
  </p>


</div>

<h1>Qwen3-0.6B-EdgeRazor-1.58bit</h1>

## Contents

- [Contents](#contents)
- [Model Overview](#model-overview)
- [Model Bit-Widths](#model-bit-widths)
- [Model Performance](#model-performance)
- [Quickstart](#quickstart)
- [Citation](#citation)

## Model Overview

- Base Model: [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
- Training: [zhangsq-nju/EdgeRazor](https://github.com/zhangsq-nju/EdgeRazor)
- Quantization: 1.58-bit for all decoder layers; 4-bit for embedding and lm_head

## Model Bit-Widths

| Mixed-Precision Recipe       | Bit-Width | This Repo    |
| ---------------------------- | --------- | ------------ |
| 100% 4-bit + 0% 1.58-bit     | 4         |              |
| 50% 4-bit + 50% 1.58-bit     | 2.79      |              |
| 12.5% 4-bit + 87.5% 1.58-bit | 1.88      |              |
| 0% 4-bit + 100% 1.58-bit     | 1.58      | ✔️ |

## Model Performance

| Models     | W-A-KV     | ARC-e | ARC-c | HellaS. | BoolQ | PIQA  | WinoG. | SIQA  | OBQA  | Tr.QA2 | Ethics | MMLU  | IFEval | GSM8K | HumanE. | Average (↑) |
| ---------- | ---------- | ----- | ----- | ------- | ----- | ----- | ------ | ----- | ----- | ------ | ------ | ----- | ------ | ----- | ------- | ----------- |
| Qwen3-0.6B | 16-16-16   | 56.02 | 34.04 | 47.23   | 64.04 | 67.36 | 56.04  | 39.20 | 31.20 | 42.84  | 47.70  | 40.12 | 58.41  | 41.54 | 37.20   | **47.35**   |
| EdgeRazor  | 4-16-16    | 58.54 | 33.45 | 45.04   | 68.01 | 68.34 | 55.72  | 40.07 | 33.40 | 43.69  | 54.36  | 39.37 | 53.42  | 42.00 | 34.15   | **47.83**   |
| EdgeRazor  | 2.79-16-16 | 51.77 | 28.33 | 37.47   | 70.70 | 63.71 | 54.06  | 40.33 | 28.20 | 42.72  | 55.08  | 36.85 | 51.39  | 26.69 | 31.10   | **44.17**   |
| EdgeRazor  | 1.88-16-16 | 51.22 | 27.73 | 34.21   | 66.91 | 63.66 | 53.35  | 38.43 | 27.60 | 43.80  | 55.92  | 28.78 | 42.51  | 25.09 | 23.17   | **41.60**   |
| EdgeRazor  | 1.58-16-16 | 45.75 | 25.77 | 33.89   | 66.64 | 60.72 | 52.33  | 38.23 | 29.80 | 44.40  | 51.70  | 32.85 | 37.34  | 14.25 | 23.17   | **39.77**   |
| EdgeRazor  | 4-8-8      | 57.79 | 33.70 | 45.00   | 67.49 | 67.85 | 55.88  | 40.17 | 33.80 | 43.53  | 54.09  | 39.73 | 53.42  | 42.00 | 34.76   | **47.80**   |
| EdgeRazor  | 2.79-8-8   | 52.10 | 28.50 | 37.36   | 70.58 | 63.92 | 53.12  | 40.12 | 28.60 | 42.82  | 54.97  | 36.44 | 49.54  | 26.99 | 32.32   | **44.10**   |
| EdgeRazor  | 1.88-8-8   | 51.47 | 27.99 | 34.22   | 66.85 | 63.49 | 53.04  | 38.02 | 27.40 | 43.88  | 55.92  | 29.56 | 44.55  | 25.09 | 23.17   | **41.76**   |
| EdgeRazor  | 1.58-8-8   | 44.87 | 26.11 | 33.88   | 66.73 | 60.55 | 51.30  | 38.28 | 31.00 | 44.72  | 50.76  | 33.09 | 38.45  | 15.01 | 22.56   | **39.81**   |

## Quickstart

It is recommended to ensure that `EdgeRazor` is installed in advance for weight-activation quantization. The provided weights are already quantized (quantized_weights*scaling_bf16); to enable activation and KV cache quantization, set `trust_remote_code=True` in the model configuration.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "zhangsq-nju/Qwen3-0.6B-EdgeRazor-1.58bit"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False # For EdgeRazor-nbit, we only train the instruct mode.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

print("thinking content:", thinking_content)
print("content:", content)
```

## Citation

If you find our project useful in your research, please consider kindly citing our papers ✏️:

```
@article{zhangsh-edgerazor,
  title={{EdgeRazor}: A Lightweight Framework for Large Language Models via Mixed-Precision Quantization-Aware Distillation},
  author={Shu-Hao Zhang and Le-Tong Huang and Xiang-Sheng Deng and Xin-Yi Zou and Chen Wu and Nan Li and Shao-Qun Zhang},
  year={2026},
}
```