Qwen2.5-Coder-32B-Instruct-gptqmodel-4bit-vortex-v1/README.md

license, license_link, language, base_model, pipeline_tag, tags

license	license_link	language	base_model	pipeline_tag	tags
apache-2.0	https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct/blob/main/LICENSE	en	Qwen/Qwen2.5-Coder-32B	text-generation	gptqmodel modelcloud code codeqwen chat qwen qwen-coder instruct int4 gptq 4bit

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:1.2.1
  • uri: https://github.com/modelcloud/gptqmodel
  • damp_percent: 0.1
  • damp_auto_increment: 0.0015

Example:

from transformers import AutoTokenizer
from gptqmodel import GPTQModel

model_name = "ModelCloud/Qwen2.5-Coder-32B-Instruct-gptqmodel-4bit-vortex-v1"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = GPTQModel.load(model_name)

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)