ModelCloud/EXAONE-3.0-7.8B-Instruct-gptq-4bit

This model has been quantized using GPTQModel.

• bits: 4
• group_size: 32
• desc_act: true
• static_groups: false
• sym: false
• 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:0.9.11-dev0"

Example:

from transformers import AutoTokenizer
from gptqmodel import GPTQModel

model_name = "ModelCloud/EXAONE-3.0-7.8B-Instruct-gptq-4bit"

prompt = [
    {"role": "system", 
     "content": "You are EXAONE model from LG AI Research, 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)

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)