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--- |
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tags: |
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- gptq |
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- 4bit |
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- int4 |
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- gptqmodel |
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- modelcloud |
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- llama-3.1 |
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- 70b |
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- instruct |
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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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- **group_size**: 128 |
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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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- **damp_percent**: 0.0025 |
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- **true_sequential**: true |
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- **model_name_or_path**: "" |
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- **model_file_base_name**: "model" |
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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:0.9.9-dev0" |
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**Here is an 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/Meta-Llama-3.1-70B-Instruct-gptq-4bit" |
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prompt = [{"role": "user", "content": "I am in Shanghai, preparing to visit the natural history museum. Can you tell me the best way to"}] |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = GPTQModel.from_quantized(model_name) |
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input_tensor = tokenizer.apply_chat_template(prompt, add_generation_prompt=True, return_tensors="pt") |
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outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=100) |
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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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``` |
