---
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
---
Qwen3-0.6B-EdgeRazor-1.88bit
## 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: 1.88-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-1.88bit"
# 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 ()
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},
journal={arXiv preprint arXiv:2605.04062}
}
```
