caT-MDC

caT-MDC is the model submitted by team caT to the MDC track of the MARS2 2026 Challenge.

The model uses the Qwen3.5-9B multimodal architecture and was post-trained with the team's cold-start and group-based reinforcement-learning pipeline. This repository contains the complete merged model in Hugging Face Transformers format rather than a LoRA adapter.

Model Details

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
• chat_template.jinja: conversation template
• args.json: archived training arguments

Installation

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

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:

Qwen3_5ForConditionalGeneration

Basic Text Inference

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.