CALISTA-INDUSTRY/gemma_3_1B_reasoning_en_ft_v1

Model Card for CALISTA-INDUSTRY/gemma_3_1B_reasoning_multimodal_en_ft_v2

Model Details

• Developed by: Mohammad Yani & Rizky Sulaeman, Politeknik Negeri Indramayu
• Model type: Fine-tuned multimodal large language model
• Language(s): English
• License: Apache 2.0
• Finetuned from: gemma-3b

Model Description

gemma_3_1B_reasoning_multimodal_en_ft_v2 is a fine-tuned version of the Gemma3 model, enhanced for multimodal reasoning tasks. It integrates both visual and textual inputs to perform complex reasoning, making it suitable for applications that require understanding and interpreting combined modalities.

Intended Uses & Limitations

Intended Uses

• Visual Question Answering (VQA)
• Image Captioning
• Multimodal Dialogue Systems
• Instruction Following with Visual Inputs

Limitations

• Performance may degrade on non-English inputs.
• May not generalize well to domains significantly different from the training data.
• Not suitable for real-time applications without further optimization.

How to Use

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="CALISTA-INDUSTRY/gemma_3_4B_reasoning_multimodal_en_ft_v2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)