Model Details

This model is an int8 model with group_size 128 and symmetric quantization of google/gemma-4-12B-it generated by intel/auto-round.

Please follow the license of the original model.

The model is quantized with OPT-RTN mode.

Transformes inference

pip uninstall gptqmodel

from transformers import AutoProcessor, AutoModelForMultimodalLM

MODEL_ID = "Intel/gemma-4-12B-it-int8-AutoRound"

# Load model
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = AutoModelForMultimodalLM.from_pretrained(
    MODEL_ID,
    dtype="auto",
    device_map="auto"
)
# Prompt - add image before text
messages = [
    {
        "role": "user", "content": [
            {"type": "image", "url": "https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.5/demo/RealWorld/RealWorld-04.png"},
            {"type": "text", "text": "What is shown in this image?"}
        ]
    }
]

# Process input
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    add_generation_prompt=True,
).to(model.device)
input_len = inputs["input_ids"].shape[-1]

# Generate output
outputs = model.generate(**inputs, max_new_tokens=512)
response = processor.decode(outputs[0][input_len:], skip_special_tokens=False)

# Parse output
print(processor.parse_response(response))

{'content': 'The image shows a large, grey stone statue of an indigenous figure, possibly an Inca, with a yellow headband, located in an urban setting. The statue is positioned in the foreground, with a densely populated city in the background, characterized by many brick-colored buildings and a hillside. In the middle ground, there's a terrace area with some plants and a neon sign that says "@rugen". The sky is clear with some white clouds.', 'role': 'assistant'}

Generate the Model

https://github.com/intel/auto-round/pull/1879 is required

auto-round
google/gemma-4-12B-it
--iters 0
--scheme W8A16

Ethical Considerations and Limitations

The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.

Therefore, before deploying any applications of the model, developers should perform safety testing.

Caveats and Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

Here are a couple of useful links to learn more about Intel's AI software:

Intel Neural Compressor

Disclaimer

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.

Cite

@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }

arxiv github