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---
tags:
- gptq
- 4bit
- int4
- gptqmodel
- modelcloud
- llama-3.1
- 8b
- base
license: llama3.1
---
This model has been quantized using [GPTQModel](https://github.com/ModelCloud/GPTQModel).
- **bits**: 4
- **group_size**: 128
- **desc_act**: true
- **static_groups**: false
- **sym**: true
- **lm_head**: false
- **damp_percent**: 0.01
- **true_sequential**: true
- **model_name_or_path**: ""
- **model_file_base_name**: "model"
- **quant_method**: "gptq"
- **checkpoint_format**: "gptq"
- **meta**：
- **quantizer**: "gptqmodel:0.9.9-dev0"
**Here is an example:**
```python
import torch
from transformers import AutoTokenizer
from gptqmodel import GPTQModel
device = torch.device("cuda:0")
model_name = "ModelCloud/Meta-Llama-3.1-8B-gptq-4bit"
prompt = "I am in Shanghai, preparing to visit the natural history museum. Can you tell me the best way to"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = GPTQModel.from_quantized(model_name)
inputs = tokenizer(prompt, return_tensors="pt").to(device)
res = model.generate(**inputs, num_beams=1, min_new_tokens=1, max_new_tokens=512)
print(tokenizer.decode(res[0]))
``` |