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@ -44,27 +44,6 @@ As shown in the chart, the R1-Onevision dataset is a carefully crafted tool desi
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.
- 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 72B.
- Parameters: For efficiency, we use a resolution of 512 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: 512
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"/>
You can load the model using the Hugging Face `transformers` library:
```python
@ -127,5 +106,26 @@ We evaluated R1-Onevision on Mathvision, Mathverse and R1-Onevision-Bench, and o
## 🏗️ Start
- 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 72B.
- Parameters: For efficiency, we use a resolution of 512 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: 512
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"/>
## 🧑‍💻 Authors
Yi Yang*, Xiaoxuan He*, Hongkun Pan*, Xiyan Jiang, Yan Deng, Xingtao Yang, Haoyu Lu, Minfeng Zhu†, Bo Zhang†, Wei Chen†