64 lines
1.7 KiB
Markdown
64 lines
1.7 KiB
Markdown
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---
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct/blob/main/LICENSE
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-Coder-32B
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pipeline_tag: text-generation
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tags:
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- gptqmodel
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- modelcloud
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- code
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- codeqwen
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- chat
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- qwen
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- qwen-coder
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- instruct
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- int4
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- gptq
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- 4bit
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---
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This model has been quantized using [GPTQModel](https://github.com/ModelCloud/GPTQModel).
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- **bits**: 4
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- **dynamic**: null
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- **group_size**: 32
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- **desc_act**: true
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- **static_groups**: false
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- **sym**: true
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- **lm_head**: false
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- **true_sequential**: true
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- **quant_method**: "gptq"
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- **checkpoint_format**: "gptq"
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- **meta**：
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- **quantizer**: gptqmodel:1.2.1
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- **uri**: https://github.com/modelcloud/gptqmodel
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- **damp_percent**: 0.1
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- **damp_auto_increment**: 0.0015
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## Example:
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```python
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from transformers import AutoTokenizer
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from gptqmodel import GPTQModel
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model_name = "ModelCloud/Qwen2.5-Coder-32B-Instruct-gptqmodel-4bit-vortex-v1"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = GPTQModel.load(model_name)
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messages = [
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{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
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{"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
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]
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input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
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outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
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result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
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print(result)
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```
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