Modelpublic

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

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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Model Card

image/png

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:

python

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)