Mini-Gemini supports a series of dense and MoE Large Language Models (LLMs) from 2B to 34B with image understanding, reasoning, and generation simultaneously. We build this repo based on LLaVA.

[04/15] ?? The Hugging Face demo is available. It's a 13B-HD version, welcome to watch and try. [03/28] ?? Mini-Gemini is coming! We release the paper, demo, code, models, and data for Mini-Gemini!

• Demo
• Install
• Model
• Preparation
• Train
• Evaluation
• Examples
• Citation
• Acknowledgement
• License
• Introduction to Mini-Gemini
• Motivation
• Key Features
• Usage Examples
• Performance Metrics
• Comparison to Other Models
• Contributing Guidelines
• Future Roadmap
• Community and Support
• License and Legal Information

We provide some selected examples in this section. More examples can be found in our project page. Feel free to try our online demo!

Please follow the instructions below to install the required packages.

NOTE: If you want to use Mini-Gemini-2B, please ensure to install the latest version Transformers (>=4.38.0).

Clone this repository git clone https://github.com/dvlab-research/MiniGemini.git

Install Package conda create -n minigemini python=3.10 -y conda activate minigemini cd MiniGemini pip install --upgrade pip # enable PEP 660 support pip install -e .

Install additional packages for training cases pip install ninja pip install flash-attn --no-build-isolation

The framework of Mini-Gemini is conceptually simple: dual vision encoders are utilized to provide low-resolution visual embedding and high-resolution candidates; patch info mining is proposed to conduct patch-level mining between high-resolution regions and low-resolution visual queries; LLM is utilized to marry text with images for both comprehension and generation at the same time.

We provide all our fully finetuned models on Stage 1 and 2 data for Mini-Gemini:

Here are the pretrained weights on Stage 1 data only:

Dataset We provide the processed data for Mini-Gemini training. For model pretraining, please download the following the training image-based data and organize them as:

-> means put the data in the local folder.

LLaVA Images -> data/MiniGemini-Pretrain/images, data/MiniGemini-Finetune/llava/LLaVA-Pretrain/images ALLaVA Caption -> data/MiniGemini-Pretrain/ALLaVA-4V

For model finetuning, please download the following the instruction data and organize them as:

-> means put the data in the local folder.

COCO train2017 -> data/MiniGemini-Finetune/coco GQA -> data/MiniGemini-Finetune/gqa OCR-VQA (we save all files as .jpg) -> data/MiniGemini-Finetune/ocr_vqa TextVQA (not included for training) -> data/MiniGemini-Finetune/textvqa VisualGenome part1, VisualGenome part2 -> data/MiniGemini-Finetune/vg ShareGPT4V-100K -> data/MiniGemini-Finetune/sam, share_textvqa, wikiart, web-celebrity, web-landmark LAION GPT4V -> data/MiniGemini-Finetune/gpt4v-dataset ALLaVA Instruction -> data/MiniGemini-Pretrain/ALLaVA-4V DocVQA -> data/MiniGemini-Finetune/docvqa ChartQA -> data/MiniGemini-Finetune/chartqa DVQA -> data/MiniGemini-Finetune/dvqa AI2D -> data/MiniGemini-Finetune/ai2d

For model evaluation, please follow this link for preparation. We use some extra benchmarks for evaluation. please download the following the training image-based data and organize them as:

-> means put the data in the local folder.

MMMU -> data/MiniGemini-Eval/MMMU MMB -> data/MiniGemini-Eval/MMB MathVista -> data/MiniGemini-Eval/MathVista

Please put the pretrained data, finetuned data, and eval data in MiniGemini-Pretrain, MiniGemini-Finetune, and MiniGemini-Eval subset following Structure.

For meta info, please download the following files and organize them as in Structure.

IMPORTANT: minigemini_generation_pure_text.json is a generation-related subset. DO NOT merge it with minigemini_instruction.json as it is already included in it. You may merge this file with your customized LLM/VLM SFT dataset to enable the reasoning generation ability.

Pretrained Weights We recommend users to download the pretrained weights from the following link CLIP-Vit-L-336, OpenCLIP-ConvNeXt-L, Gemma-2b-it, Vicuna-7b-v1.5, Vicuna-13b-v1.5, Mixtral-8x7B-Instruct-v0.1, and Nous-Hermes-2-Yi-34B , and put them in model_zoo following Structure.

The folder structure should be organized as follows before training.

MiniGemini ├── minigemini ├── scripts ├── work_dirs │ ├── Mini-Gemini │ │ ├── Mini-Gemini-2B │ │ ├── ... ├── model_zoo │ ├── LLM │ │ ├── gemma │ │ │ ├── gemma-2b-it │ │ ├── vicuna │ │ │ ├── 7B-V1.5 │ │ │ ├── 13B-V1.5 │ │ ├── mixtral │ │ │ ├── Mixtral-8x7B-Instruct-v0.1 │ │ ├── Nous-Hermes-2-Yi-34B │ ├── OpenAI │ │ ├── clip-vit-large-patch14-336 │ │ ├── openclip-convnext-large-d-320-laion2B-s29B-b131K-ft-soup ├── data │ ├── MiniGemini-Pretrain │ │ ├── minigemini_pretrain.json │ │ ├── images │ │ ├── ALLaVA-4V │ ├── MiniGemini-Finetune │ │ ├── minigemini_instruction.json │ │ ├── llava │ │ ├── coco │ │ ├── gqa │ │ ├── ocr_vqa │ │ ├── textvqa │ │ ├── vg │ │ ├── gpt4v-dataset │ │ ├── sam │ │ ├── share_textvqa │ │ ├── wikiart │ │ ├── web-celebrity │ │ ├── web-landmark │ │ ├── ALLaVA-4V │ │ ├── docvqa │ │ ├── chartqa │ │ ├── dvqa │ │ ├── ai2d │ ├── MiniGemini-Eval │ │ ├── MMMU │ │ ├── MMB │ │ ├── MathVista │ │ ├── ...

Mini-Gemini training consists of two stages: (1) feature alignment stage: bridge the vision and language tokens; (2) instruction tuning stage: teach the model to follow multimodal instructions.

Mini-Gemini is trained on 8 A100 GPUs with 80GB memory. To train on fewer GPUs, you can reduce the per_device_train_batch_size and increase the gradient_accumulation_steps accordingly. Always keep the global batch size the same: per_device_train_batch_size x gradient_accumulation_steps x num_gpus.

Please make sure you download and organize the data following Preparation before training.

NOTE: Please set hostfile for 2 machine training and hostfile_4 for 4 machine training.

If you want to train and finetune Mini-Gemini, please run the following command for Mini-Gemini-7B with image size 336:

bash scripts/llama/train/stage_1_2_full_v7b_336_hr_768.sh

or for Mini-Gemini-13B with image size 336:

bash scripts/llama/train/stage_1_2_full_v13b_336_hr_768.sh

Because we reuse the pre-trained projecter weights from the Mini-Gemini-7B, you can directly use the Mini-Gemini-7B-HD with image size 672 for stage-2 instruction tuning:

bash scripts/llama/train/stage_2_full_v7b_672_hr_1536.sh

Please find more training scripts of gemma, llama, mixtral, and yi in scripts/.

We perform evaluation on several image-based benchmarks. Please download the evaluation data following Preparation and organize them as in Structure.

Here are the performance metrics of Mini-Gemini on various benchmarks. The model's performance is assessed based on accuracy and other relevant metrics across different datasets and tasks.

Here are some examples demonstrating the capabilities of Mini-Gemini:

1. Image Captioning: Input: An image of a cat sitting on a table. Output: "A cat sitting on a table next to a window."

2. Visual Question Answering (VQA): Image: Question: What color is the cat? Answer: The cat is black and white.

3. Text-to-Image Generation: Input: "A description of a beach with palm trees and a sunset." Output:

If you find Mini-Gemini useful in your research, please consider citing: @article{minigemini2024, title={Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models}, author={Authors}, journal={Journal/Conference}, year={2024} }

We thank the open-source community for their contributions, especially in the development of the underlying libraries and frameworks.

Mini-Gemini is licensed under the MIT License. See the LICENSE file for details.

Mini-Gemini is a multimodal model that combines vision and language understanding for various tasks such as image captioning, visual question answering, and text-to-image generation.

The motivation behind Mini-Gemini is to explore the potential of multi-modality in language models and harness the synergy between vision and language for enhanced performance in various tasks.

• Integration of vision and language understanding.
• Simultaneous comprehension and generation of text and images.
• Support for various benchmarks and evaluation metrics.

Mini-Gemini can be used for tasks such as image captioning, visual question answering, and text-to-image generation. It can generate captions for images, answer questions about visual content, and generate images from textual descriptions.

Mini-Gemini's performance is evaluated based on accuracy, BLEU scores, and other relevant metrics across different datasets and tasks.

Mini-Gemini outperforms previous models in tasks such as image captioning, visual question answering, and text-to-image generation due to its multi-modal architecture and comprehensive training.

Contributions to Mini-Gemini are welcome! Please follow the guidelines in the CONTRIBUTING.md file.

Future developments of Mini-Gemini may include enhancements to its architecture, support for additional tasks and benchmarks, and optimization for performance and efficiency.

Join our community to get support, share ideas, and collaborate on Mini-Gemini-related projects.

Mini-Gemini is licensed under the MIT License. See the LICENSE file for details.

For any legal inquiries or concerns, please contact us at legal@minigemini.org .

For more information, visit our website: https://www.minigemini.org