|
--- |
|
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 |
|
- qwen |
|
- instruct |
|
- int4 |
|
- gptq |
|
- 4bit |
|
--- |
|
|
|
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(model_name) |
|
model = GPTQModel.load("ModelCloud/QwQ-32B-Preview-GPTQ-4bit") |
|
|
|
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) |
|
``` |
