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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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- 8b |
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- base |
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license: llama3.1 |
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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**: 0.01 |
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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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import torch |
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from transformers import AutoTokenizer |
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from gptqmodel import GPTQModel |
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device = torch.device("cuda:0") |
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model_name = "ModelCloud/Meta-Llama-3.1-8B-gptq-4bit" |
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prompt = "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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inputs = tokenizer(prompt, return_tensors="pt").to(device) |
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res = model.generate(**inputs, num_beams=1, min_new_tokens=1, max_new_tokens=512) |
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print(tokenizer.decode(res[0])) |
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``` |
