---
base_model: Qwen/Qwen2-VL-7B-Instruct
library_name: transformers
license: apache-2.0
tags:
- llama-factory
- full
- generated_from_trainer
- long-context
- reasoning
- multi-modal
model-index:
- name: TVC-7B
  results: []
pipeline_tag: image-text-to-text
---

## Model Summary

The TVC models are 7B parameter models based on Qwen2-VL-7B-Instruct model with a context window of 8K tokens.

- **Repository:** https://github.com/sun-hailong/TVC
- **Project Page:** https://sun-hailong.github.io/projects/TVC/
- **Languages:** English, Chinese
- **Paper:** https://arxiv.org/abs/2503.13360

### Model Architecture

- **Architecture:** Qwen2-VL-7B-Instruct
- **Data:** a mixture of 300k long-chain reasoning data
- **Precision:** BFloat16

#### Hardware & Software

- **Hardware:** 64 * NVIDIA Tesla H20
- **Orchestration:** HuggingFace Trainer
- **Code:** Pytorch

### Framework versions

- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3

## Quick Start

```python
from vllm import LLM, SamplingParams
from PIL import Image

model_name = "Allen8/TVC-72B"
llm = LLM(
        model=model_name,
        trust_remote_code=True,
        tensor_parallel_size=8,
    )

question = "Hint: Please answer the question requiring an integer answer and provide the final value, e.g., 1, 2, 3, at the end.
Question: Subtract all red things. Subtract all tiny matte balls. How many objects are left?
Please answer the question using a long-chain reasoning style and think step by step."
placeholder = "<|image_pad|>"
prompt = ("<|im_start|>system
You are a helpful assistant.<|im_end|>
"
f"<|im_start|>user
<|vision_start|>{placeholder}<|vision_end|>"
f"{question}<|im_end|>
"
"<|im_start|>assistant
")

sampling_params = SamplingParams(
    temperature=0.0,
    top_k=1,
    top_p=1.0,
    stop_token_ids=[],
    repetition_penalty=1.05,
    max_tokens=8192
)

image = Image.open("images/case1.png")
inputs = {
            "prompt": prompt,
            "multi_modal_data": {
                "image": image
            },
        }

outputs = llm.generate([inputs], sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
```

## Citation

```
@article{sun2024mitigating,
    title={Mitigating Visual Forgetting via Take-along Visual Conditioning for Multi-modal Long CoT Reasoning},
    author={Sun, Hai-Long and Sun, Zhun and Peng, Houwen and Ye, Han-Jia},
    journal={arXiv preprint arXiv:2503.13360},
    year={2025}
}
```