cabbagel

caT-MDC

README

License: apache-2.0

Model Details

Table with columns: Item, Description
Item	Description
Team	caT
Challenge	MARS2 2026
Track	MDC
Backbone	Qwen3.5-9B
Architecture	`Qwen3_5ForConditionalGeneration`
Model type	Multimodal vision-language model
Weight format	Safetensors
Precision	BFloat16
Training stage	Cold-start post-training followed by GSPO-stage reinforcement learning
Release format	Complete merged model

Training Summary

The released checkpoint is the selected MDC submission model. According to the archived configuration in args.json, its reinforcement-learning stage used:

learning rate: 1e-5
epochs: 1
per-device batch size: 4
gradient accumulation steps: 2
rollout generations per prompt: 8
maximum completion length: 8048
precision: BFloat16
optimizer: fused AdamW
learning-rate schedule: cosine
experiment tracking: SwanLab and TensorBoard

The competition training dataset is not redistributed in this model repository.

Repository Contents

config.json: model architecture and configuration
generation_config.json: default generation configuration
model-*.safetensors: sharded model weights
model.safetensors.index.json: weight index
preprocessor_config.json: multimodal preprocessing configuration
processor_config.json: processor configuration
tokenizer.json: tokenizer
tokenizer_config.json: tokenizer configuration
: conversation template

Installation

bash
pip install -U transformers accelerate pillow

Qwen3.5 requires a recent Transformers version. Refer to the official Qwen3.5-9B model card for current compatibility guidance.

Loading the Model

python
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

model_id = "cabbagel/caT-MDC"

processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
)

print(model.__class__.__name__)

Expected model class:

text
Qwen3_5ForConditionalGeneration

Basic Text Inference

python
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

model_id = "cabbagel/caT-MDC"

processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Briefly describe your multimodal reasoning capabilities.",
            }
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=256)
output_ids = generated_ids[:, inputs["input_ids"].shape[1]:]

response = processor.batch_decode(
    output_ids,
    skip_special_tokens=True,
)[0]

print(response)

For image and video inputs, follow the multimodal message format documented in the official Qwen3.5 model card.

Intended Use

This model is released for:

reproduction and verification of the caT MDC submission;
research on multimodal understanding and reasoning;
evaluation within the MARS2 MDC task setting.

Limitations

The model was optimized for the MDC competition setting and may not generalize to unrelated tasks.
The model may produce inaccurate or unsupported responses.
No claim is made that the model is suitable for safety-critical or high-stakes applications.
Users should independently verify model outputs.

Acknowledgements

This work builds on Qwen3.5-9B. We thank the Qwen team and the MARS2 2026 organizers.

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Model Details

Model Provider

cabbagel

Model Tree

Base

Qwen/Qwen3.5-9B

Fine-tuned

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality

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README

License: apache-2.0

Model Details

Table with columns: Item, Description
Item	Description
Team	caT
Challenge	MARS2 2026
Track	MDC
Backbone	Qwen3.5-9B
Architecture	`Qwen3_5ForConditionalGeneration`
Model type	Multimodal vision-language model
Weight format	Safetensors
Precision	BFloat16
Training stage	Cold-start post-training followed by GSPO-stage reinforcement learning
Release format	Complete merged model

Training Summary

The released checkpoint is the selected MDC submission model. According to the archived configuration in args.json, its reinforcement-learning stage used:

learning rate: 1e-5
epochs: 1
per-device batch size: 4
gradient accumulation steps: 2
rollout generations per prompt: 8
maximum completion length: 8048
precision: BFloat16
optimizer: fused AdamW
learning-rate schedule: cosine
experiment tracking: SwanLab and TensorBoard

The competition training dataset is not redistributed in this model repository.

Repository Contents

config.json: model architecture and configuration
generation_config.json: default generation configuration
model-*.safetensors: sharded model weights
model.safetensors.index.json: weight index
preprocessor_config.json: multimodal preprocessing configuration
processor_config.json: processor configuration
tokenizer.json: tokenizer
tokenizer_config.json: tokenizer configuration
: conversation template

Installation

bash
pip install -U transformers accelerate pillow

Qwen3.5 requires a recent Transformers version. Refer to the official Qwen3.5-9B model card for current compatibility guidance.

Loading the Model

python
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

model_id = "cabbagel/caT-MDC"

processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
)

print(model.__class__.__name__)

Expected model class:

text
Qwen3_5ForConditionalGeneration

Basic Text Inference

python
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

model_id = "cabbagel/caT-MDC"

processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Briefly describe your multimodal reasoning capabilities.",
            }
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=256)
output_ids = generated_ids[:, inputs["input_ids"].shape[1]:]

response = processor.batch_decode(
    output_ids,
    skip_special_tokens=True,
)[0]

print(response)

For image and video inputs, follow the multimodal message format documented in the official Qwen3.5 model card.

Intended Use

This model is released for:

reproduction and verification of the caT MDC submission;
research on multimodal understanding and reasoning;
evaluation within the MARS2 MDC task setting.

Limitations

The model was optimized for the MDC competition setting and may not generalize to unrelated tasks.
The model may produce inaccurate or unsupported responses.
No claim is made that the model is suitable for safety-critical or high-stakes applications.
Users should independently verify model outputs.

Acknowledgements

This work builds on Qwen3.5-9B. We thank the Qwen team and the MARS2 2026 organizers.