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"
You can use GPTQModel for model inference.
import torch
from transformers import AutoTokenizer, GenerationConfig
from gptqmodel import GPTQModel
model_name = "ModelCloud/DeepSeek-V2-Chat-0628-gptq-4bit"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
# `max_memory` should be set based on your devices
max_memory = {i: "75GB" for i in range(2)}
# `device_map` cannot be set to `auto`
model = GPTQModel.from_quantized(model_name, trust_remote_code=True, device_map="sequential", max_memory=max_memory, torch_dtype=torch.float16, attn_implementation="eager")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Write a piece of quicksort code in C++"}
]
input_tensor = tokenizer.apply_chat_template(messages, 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)