| ## CodeFuse-VLM | |
| CodeFuse-VLM is a Multimodal LLM(MLLM) framework that provides users with multiple vision encoders, multimodal alignment adapters, and LLMs. Through CodeFuse-VLM framework, users are able to customize their own MLLM model to adapt their own tasks. | |
| As more and more models are published on Huggingface community, there will be more open-source vision encoders and LLMs. Each of these models has their own specialties, e.g. Code-LLama is good at code-related tasks but has poor performance for Chinese tasks. Therefore, we built CodeFuse-VLM framework to support multiple vision encoders, multimodal alignment adapters, and LLMs to adapt different types of tasks. | |
| <p align="center"> | |
| <img src="./CodeFuse-VLM-arch.png" width="50%" /> | |
| </p> | |
| Under CodeFuse-VLM framework, we use cross attention multimodal adapter, Qwen-14B LLM, and Qwen-VL's vision encoder to train CodeFuse-VLM-14B model. On multiple benchmarks, our CodeFuse-VLM-14B shows superior performances over Qwen-VL and LLAVA-1.5. | |
| <p align="center"> | |
| <img src="./CodeFuse-VLM-14B-performance.png" width="50%" /> | |
| </p> | |
| Here is the table for different MLLM model's performance on benchmarks | |
| Model | MMBench | MMBench-CN | VqaV2 | GQA | TextVQA | Vizwiz | |
| | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | | |
| LLAVA-1.5 | 67.7 | 63.6 | 80.0 | 63.3 | 61.3 | 53.6 | |
| Qwen-VL | 60.6 | 56.7 | 78.2 | 57.5 | 63.8 | 38.9 | |
| CodeFuse-VLM-14B | 75.7 | 69.8 | 79.3 | 59.4 | 63.9 | 45.3 | |
| ## Contents | |
| - [Install](#Install) | |
| - [Datasets](#Datasets) | |
| - [Multimodal Alignment](#Multimodal-Alignment) | |
| - [Visual Instruction Tuning](#Visual-Instruction-Tuning) | |
| - [Evaluation](#Evaluation) | |
| ## Install | |
| Please run sh init\_env.sh | |
| ## Datasets | |
| Here's the table of datasets we used to train CodeFuse-VLM-14B: | |
| Dataset | Task Type | Number of Samples | |
| | ------------- | ------------- | ------------- | | |
| synthdog-en | OCR | 800,000 | |
| synthdog-zh | OCR | 800,000 | |
| cc3m(downsampled)| Image Caption | 600,000 | |
| cc3m(downsampled)| Image Caption | 600,000 | |
| SBU | Image Caption | 850,000 | |
| Visual Genome VQA (Downsampled) | Visual Question Answer(VQA) | 500,000 | |
| Visual Genome Region descriptions (Downsampled) | Reference Grouding | 500,000 | |
| Visual Genome objects (Downsampled) | Grounded Caption | 500,000 | |
| OCR VQA (Downsampled) | OCR and VQA | 500,000 | |
| Please download these datasets on their own official websites. | |
| ## Multimodal Alignment | |
| Please run sh scripts/pretrain.sh or sh scripts/pretrain\_multinode.sh | |
| ## Visual Instruction Tuning | |
| Please run sh scripts/finetune.sh or sh scripts/finetune\_multinode.sh | |
| ## Evaluation | |
| Please run python scrips in directory llava/eval/ | |