ModelCloud / GRIN-MoE-gptq-4bit

This model has been quantized using GPTQModel .

• bits : 4
• group_size : 128
• desc_act : false
• static_groups : false
• sym : true
• lm_head : false
• damp_percent : 0.0025
• damp_auto_increment : 0.0015
• true_sequential : true
• model_name_or_path : ""
• model_file_base_name : "model"
• quant_method : "gptq"
• checkpoint_format : "gptq"
• meta ：
  • quantizer : "gptqmodel:1.0.3-dev0"

Example:

from transformers import AutoTokenizer
from gptqmodel import GPTQModel

model_name = "ModelCloud/GRIN-MoE-gptq-4bit"

prompt = [
    {"role": "system", 
     "content": "You are GRIN-MoE model from microsoft, a helpful assistant."},
    {"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, trust_remote_code=True)

model = GPTQModel.from_quantized(model_name, trust_remote_code=True)

input_tensor = tokenizer.apply_chat_template(prompt, 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)

Lm_eval result: