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metadata
tags:
  - gptq
  - 4bit
  - gptqmodel
  - modelcloud
  - llama-3.1
  - 8b

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]))