🐛 Update: We have discovered a regression in the QwQ Vortext v1 model that may output extra strings such as "Edited Text" that was result of invalid calibration data auto-injected by our vortex pipeline.
Please use v2 of the model here: https://huggingface.co/ModelCloud/QwQ-32B-Preview-gptqmodel-4bit-vortex-v2

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)