ModelCloud/QwQ-32B-Preview-gptqmodel-4bit-vortex-v1

🐛 Update: We have discovered a regression in the QwQ quant that may output extra strings such as "Edited Text" that was result of invalid calibration data auto-injected by our vortex pipeline. v2 of the quant is undergoing benchmark evaluations and will be released soon.

This model has been quantized using GPTQModel.

• bits: 4
• dynamic: null
• group_size: 32
• desc_act: true
• static_groups: false
• sym: true
• lm_head: false
• true_sequential: true
• quant_method: "gptq"
• checkpoint_format: "gptq"
• meta：
  • quantizer: gptqmodel:1.2.2
  • uri: https://github.com/modelcloud/gptqmodel
  • damp_percent: 0.1
  • damp_auto_increment: 0.0015

Example:

from transformers import AutoTokenizer
from gptqmodel import GPTQModel

tokenizer = AutoTokenizer.from_pretrained("ModelCloud/QwQ-32B-Preview-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/QwQ-32B-Preview-gptqmodel-4bit-vortex-v1")

messages = [
    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
    {"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
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=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)

print(result)