ModelCloud/gemma-2-27b-it-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"

Currently, only vllm can load the quantized gemma2-27b for proper inference. Here is an example:

import os
# Gemma-2 use Flashinfer backend for models with logits_soft_cap. Otherwise, the output might be wrong.
os.environ['VLLM_ATTENTION_BACKEND'] = 'FLASHINFER'

from transformers import AutoTokenizer
from gptqmodel import BACKEND, GPTQModel

model_name = "ModelCloud/gemma-2-27b-it-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,
            backend=BACKEND.VLLM,
        )

inputs = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
outputs = model.generate(prompts=inputs, temperature=0.95, max_length=128)
print(outputs[0].outputs[0].text)