README.md

---
license: apache-2.0
datasets:
- Fancy-MLLM/R1-Onevision
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
pipeline_tag: image-text-to-text
---

## R1-Onevision

[\[📂 GitHub\]](https://github.com/Fancy-MLLM/R1-Onevision)[\[📝 Report\]](https://yangyi-vai.notion.site/r1-onevision?pvs=4)
[\[🤗 HF Dataset\]](https://huggingface.co/datasets/Fancy-MLLM/R1-onevision)  [\[🤗 Reasoning Benchmark\]](https://huggingface.co/datasets/Fancy-MLLM/R1-OneVision-Bench) [\[🤗 HF Demo\]](https://huggingface.co/spaces/Fancy-MLLM/R1-OneVision)    

## Model Overview

This is a multimodal large language model fine-tuned from Qwen2.5-VL on the **R1-Onevision** dataset. The model enhances vision-language understanding and reasoning capabilities, making it suitable for various tasks such as visual reasoning, image understanding. With its robust ability to perform multimodal reasoning, R1-Onevision emerges as a powerful AI assistant capable of addressing a wide range of problem-solving challenges across different domains.

## Training Configuration and Curve
- Framework: The training process uses the open-source **LLama-Factory** library, with **Qwen2.5-VL-Instruct** as the base model. This model comes in three variants: 3B, 7B, and 32B.
- Parameters: For efficiency, we use a resolution of 518 for image inputs to save GPU memory. The training follows a full model SFT (Supervised Fine-Tuning) approach with a learning rate of 1e-5, trained for one epoch.
    
The training configuration is as follows:
```python
image_resolution: 518
cutoff_len: 8192
per_device_train_batch_size: 1
gradient_accumulation_steps: 16
learning_rate: 1.0e-5

num_train_epochs: 1.0
lr_scheduler_type: cosine
warmup_ratio: 0.05
bf16: true
flash_attn: fa2
```

Training loss curve:
<img src="https://cdn-uploads.huggingface.co/production/uploads/65af78bb3e82498d4c65ed2a/8BNyo-v68aFvab2kXxtt1.png"/>

## Usage

You can load the model using the Hugging Face `transformers` library:

```python
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
import torch
from qwen_vl_utils import process_vision_info

MODEL_ID = "Fancy-MLLM/R1-Onevision-7B"
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16
).to("cuda").eval()

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "<your image path>"},
            {"type": "text", "text": "Hint: Please answer the question and provide the final answer at the end. Question: Which number do you have to write in the last daisy?"},
        ],
    }
]

# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=4096)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```

## Ongoing Work
1. **Rule-Based Reinforcement Learning (RL)**
    
    We are actively exploring the integration of rule-based systems into reinforcement learning to enhance the agent's decision-making process. This approach combines domain-specific rules with the learning process, aiming to improve the efficiency and safety of learning in complex environments.
    
2. **Training with General Data and Multimodal Reasoning CoT**
    
    Our ongoing work includes expanding the training datasets by incorporating more general data alongside multimodal reasoning Chain-of-Thought (CoT) data. This will enable the model to benefit from a broader range of information, enhancing its ability to handle diverse reasoning tasks across various domains.
    
3. **Incorporating Chinese Multimodal Reasoning CoT Data**
    
    We are also focused on integrating Chinese multimodal reasoning CoT data into the training process. By adding this language-specific dataset, we aim to improve the model’s capability to perform reasoning tasks in Chinese, expanding its multilingual and multimodal reasoning proficiency.
    
4. **Release of the 3B Model**

    
    We are working on the release of a smaller, more efficient 3B model, which is designed to provide a balance between performance and resource efficiency. This model aims to deliver strong multimodal reasoning capabilities while being more accessible and optimized for environments with limited computational resources, offering a more compact alternative to the current 7B model.

# Institution
- Zhejiang University

## Model Contact
- xiaoxuanhe@zju.edu.cn
- panhongkun@zju.edu.cn
- yang-yi@zju.edu.cn