R1-Onevision-7B
| Property | Value |
|---|---|
| Developer | Fancy-MLLM (Zhejiang University) |
| Model Size | 7B parameters |
| Base Model | Qwen2.5-VL |
| Training Framework | LLama-Factory |
| Image Resolution | 518px |
| Model Access | Hugging Face |
What is R1-Onevision-7B?
R1-Onevision-7B is a sophisticated multimodal large language model that enhances vision-language understanding and reasoning capabilities. Fine-tuned from Qwen2.5-VL, this model represents a significant advancement in multimodal AI, capable of processing both textual and visual information for complex reasoning tasks.
Implementation Details
The model utilizes a full model Supervised Fine-Tuning (SFT) approach with carefully optimized parameters. Training configuration includes a 518px image resolution to balance performance and GPU memory usage, with a learning rate of 1e-5 and cosine scheduler with 5% warmup ratio. The model supports bfloat16 precision and implements Flash Attention 2 for improved efficiency.
- Batch processing with gradient accumulation over 16 steps
- Context length of 8192 tokens
- Optimized for both CPU and GPU environments
- Implements efficient vision-language processing pipeline
Core Capabilities
- Advanced visual reasoning and understanding
- Multimodal problem-solving across domains
- Efficient image processing with optimized resolution
- Robust vision-language alignment
Frequently Asked Questions
Q: What makes this model unique?
R1-Onevision-7B stands out for its specialized fine-tuning on vision-language tasks and its efficient implementation using cutting-edge technologies like Flash Attention 2. The model offers a balanced approach between computational efficiency and performance.
Q: What are the recommended use cases?
The model excels in visual reasoning tasks, image understanding, and multimodal problem-solving scenarios. It's particularly suitable for applications requiring sophisticated vision-language interaction and reasoning capabilities.
