---
license: apache-2.0
license_link: https://huggingface.co/Qwen/QwQ-32B-Preview/blob/main/LICENSE
language:
- en
base_model:
- Qwen/QwQ-32B-Preview
pipeline_tag: text-generation
tags:
- gptqmodel
- modelcloud
- chat
- qwen2
- qwq
- instruct
- int4
- gptq
- 4bit
---


<center> 
  🐛 Update: We have discovered a regression in the QwQ Vortext v1 model that may output extra strings such as "Edited Text" that was result of invalid calibration data auto-injected by our vortex pipeline. <br>
  Please use v2 of the model here: <a href="https://huggingface.co/ModelCloud/QwQ-32B-Preview-gptqmodel-4bit-vortex-v2">https://huggingface.co/ModelCloud/QwQ-32B-Preview-gptqmodel-4bit-vortex-v2</a>
</center>

![image/png](https://cdn-uploads.huggingface.co/production/uploads/641c13e7999935676ec7bc03/NV7rN-pih5sApVOZeDXSJ.png)

This model has been quantized using [GPTQModel](https://github.com/ModelCloud/GPTQModel).

- **bits**: 4
- **dynamic**: null
- **group_size**: 32
- **desc_act**: true
- **static_groups**: false
- **sym**: true
- **lm_head**: false
- **true_sequential**: true
- **quant_method**: "gptq"
- **checkpoint_format**: "gptq"
- **meta**：
  - **quantizer**: gptqmodel:1.2.2
  - **uri**: https://github.com/modelcloud/gptqmodel
  - **damp_percent**: 0.1
  - **damp_auto_increment**: 0.0015


## Example:
```python
from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/QwQ-32B-Preview-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/QwQ-32B-Preview-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)
```