README.md

---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct/blob/main/LICENSE
language:
- en
base_model:
- Qwen/Qwen2.5-Coder-32B
pipeline_tag: text-generation
tags:
- gptqmodel
- modelcloud
- code
- codeqwen
- chat
- qwen
- qwen-coder
- instruct
- int4
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/641c13e7999935676ec7bc03/-n_0DiARmihJh8GH96YaX.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:1.2.1
  - **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

model_name = "ModelCloud/Qwen2.5-Coder-32B-Instruct-gptqmodel-4bit-vortex-v1"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = GPTQModel.load(model_name)

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)
```