| | --- |
| | license: apache-2.0 |
| | base_model: |
| | - Qwen/Qwen2.5-7B-Instruct |
| | pipeline_tag: text-generation |
| | language: |
| | - en |
| | - zh |
| | --- |
| | # Insight-V-Summary |
| |
|
| | ## Model Summary |
| |
|
| | The Insight-V models are 7B parameter models based on Qwen2.5 language model with a context window of 32K tokens. |
| |
|
| | Insight-V offers **1)** a scalable data generation pipeline for long-chain, high-quality reasoning data, **2)** a multi-agent system that decomposes visual reasoning tasks into reasoning and summarization, and **3)** a two-stage training pipeline to enhance visual reasoning capabilities. Together, these contributions address key challenges in visual reasoning, providing a solid foundation for future research in MLLM reasoning. |
| |
|
| | - **Repository:** https://github.com/dongyh20/Insight-V |
| | - **Languages:** English, Chinese |
| | - **Paper:** https://arxiv.org/abs/2411.14432 |
| |
|
| |
|
| | ### Model Architecture |
| |
|
| | - **Architecture:** Pre-trained [Oryx-ViT](https://huggingface.co/THUdyh/Oryx-ViT) + Qwen2.5-7B |
| | - **Data:** a mixture of 1.2M image-text data |
| | - **Precision:** BFloat16 |
| |
|
| | #### Hardware & Software |
| |
|
| | - **Hardware:** 64 * NVIDIA Tesla A100 |
| | - **Orchestration:** HuggingFace Trainer |
| | - **Code:** Pytorch |
| |
|
| | ## Citation |
