metadata
tags:
- gptq
- 4bit
- int4
- gptqmodel
- modelcloud
- llama-3.1
- 70b
- instruct
This model has been quantized using GPTQModel.
- bits: 4
- group_size: 128
- desc_act: true
- static_groups: false
- sym: true
- lm_head: false
- damp_percent: 0.0025
- 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:
from transformers import AutoTokenizer
from gptqmodel import GPTQModel
model_name = "ModelCloud/Meta-Llama-3.1-70B-Instruct-gptq-4bit"
prompt = [{"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)
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)