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,)
print(outputs[0].outputs[0].text)