InternLM-XComposer2.5
| Property | Value |
|---|---|
| Model Size | 7B parameters |
| License | Apache-2.0 (code), Custom (weights) |
| Paper | Research Paper |
| Training Context | 24K interleaved image-text contexts |
What is internlm-xcomposer2d5-7b?
InternLM-XComposer2.5 is a state-of-the-art vision-language model that achieves GPT-4V level capabilities using only a 7B parameter LLM backend. The model excels in various text-image comprehension and composition tasks, with the ability to handle extended context lengths up to 96K through RoPE extrapolation.
Implementation Details
The model is implemented using PyTorch and the Transformers library, featuring a sophisticated architecture that enables seamless processing of both visual and textual inputs. It's trained on 24K interleaved image-text contexts and can be easily deployed using the Hugging Face Transformers framework.
- Supports bfloat16 and float32 precision
- Implements advanced RoPE extrapolation for extended context handling
- Features custom tokenizer and model architecture for optimal performance
Core Capabilities
- Video Understanding and Analysis
- Multi-Image Multi-Turn Dialog
- High Resolution Image Understanding
- Instruction to Webpage Conversion
- Resume to Webpage Generation
- Screenshot to Webpage Translation
- Article Writing and Content Generation
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to achieve GPT-4V level capabilities with only 7B parameters, combined with its extensive 96K context length support and versatile multi-modal applications, sets it apart from other vision-language models.
Q: What are the recommended use cases?
The model excels in various applications including video analysis, image understanding, webpage generation, content creation, and multi-modal dialogue systems. It's particularly useful for tasks requiring detailed visual understanding and text generation.
