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thomasip/Qwen3-Omni-30B-A3B-Instruct-GPTQ-4bit
Qwen3-Omni-30B-A3B-Instruct-GPTQ-4bit is a machine learning model from thomasip. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This is a 4-bit GPTQ quant for Qwen/Qwen3-Omni-30B-A3B-Instruct.
Downloads · 30 days
527
7% of all-time downloads
All-time downloads
7.4K
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35.3B
26.4 GB on disk
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3
Public
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.safetensors26.4 GB · 100%
How the weights are stored.
I3229.9B · 85%
From the Hugging Face model README
This is a 4-bit GPTQ quant for Qwen/Qwen3-Omni-30B-A3B-Instruct.
Quantized using the following script:
from datasets import load_dataset
from gptqmodel import GPTQModel, QuantizeConfig
model_id = "Qwen/Qwen3-Omni-30B-A3B-Instruct"
quant_path = "Qwen3-Omni-30B-A3B-Instruct-GPTQ-4bit"
calibration_dataset = load_dataset(
"allenai/c4",
data_files="en/c4-train.00001-of-01024.json.gz",
split="train"
)
calibration_dataset = calibration_dataset.filter(lambda x: 256 <= len(x["text"]) <= 2048)
calibration_dataset = calibration_dataset.select(range(1024))["text"]
quant_config = QuantizeConfig(bits=4, group_size=128)
model = GPTQModel.load(model_id, quant_config)
model.quantize(calibration_dataset, batch_size=8)
model.save(quant_path)