ModelCloud/Meta-Llama-3.1-8B-gptq-4bit

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: 0.01
• 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:

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]))