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--- |
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license: apache-2.0 |
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license_link: https://huggingface.co/Qwen/QwQ-32B-Preview/blob/main/LICENSE |
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language: |
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- en |
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base_model: |
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- Qwen/QwQ-32B-Preview |
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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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- chat |
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- qwen2 |
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- qwq |
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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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## 🐛 Update: We have discovered a regression in the QwQ quant that may output extra strings such as "Edited Text" that was result of invalid calibration data auto-injected by our vortex pipeline. v2 of the quant is undergoing benchmark evaluations and will be released soon. |
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/641c13e7999935676ec7bc03/NV7rN-pih5sApVOZeDXSJ.png) |
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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:1.2.2 |
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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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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = GPTQModel.load("ModelCloud/QwQ-32B-Preview-GPTQ-4bit") |
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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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``` |