File size: 1,391 Bytes
804d459 e77f045 804d459 d0fb2f7 804d459 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
---
tags:
- gptq
- 4bit
- int4
- gptqmodel
- modelcloud
- instruct
- exaone
---
This model has been quantized using [GPTQModel](https://github.com/ModelCloud/GPTQModel).
- **bits**: 4
- **group_size**: 32
- **desc_act**: true
- **static_groups**: false
- **sym**: false
- **lm_head**: false
- **damp_percent**: 0.0025
- **damp_auto_increment**: 0.0015
- **true_sequential**: true
- **model_name_or_path**: ""
- **model_file_base_name**: "model"
- **quant_method**: "gptq"
- **checkpoint_format**: "gptq"
- **meta**：
- **quantizer**: "gptqmodel:0.9.11-dev0"
## Example:
```python
from transformers import AutoTokenizer
from gptqmodel import GPTQModel
model_name = "ModelCloud/EXAONE-3.0-7.8B-Instruct-gptq-4bit"
prompt = [
{"role": "system",
"content": "You are EXAONE model from LG AI Research, a helpful assistant."},
{"role": "user", "content": "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, trust_remote_code=True)
input_tensor = tokenizer.apply_chat_template(prompt, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
``` |