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-<h2 align="center"> <a href="https://arxiv.org/abs/2402.14289">TinyLLaVA: A Framework of Small-scale Large Multimodal Models</a>
-
-<h5 align="center">
-
-[![hf_space](https://img.shields.io/badge/🤗-%20Open%20In%20HF-blue.svg)](https://huggingface.co/bczhou/TinyLLaVA-3.1B) [![arXiv](https://img.shields.io/badge/Arxiv-2402.14289-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2402.14289) [![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](https://github.com/PKU-YuanGroup/MoE-LLaVA/blob/main/LICENSE) 
-
-
-## &#x1F389; News
-* **[2024.03.10]**  base recipe out!
-* **[2024.03.10]**  Finetune scripts out!
-* **[2024.02.25]**  Update evaluation scripts and docs!
-* **[2024.02.25]**  Data descriptions out. Release TinyLLaVA-1.5B and TinyLLaVA-2.0B!
-* **[2024.02.24]**  Example code on inference and model loading added!
-* **[2024.02.23]**  Evaluation code and scripts released!
-* **[2024.02.21]**  Creating the [TinyLLaVABench](https://github.com/DLCV-BUAA/TinyLLavaBench) repository on GitHub!
-* **[2024.02.21]**  Our paper: [TinyLLaVA: A Framework of Small-scale Large Multimodal Models](https://arxiv.org/abs/2402.14289) is out!
-* **[2024.01.11]**  Our fist model [TinyLLaVA-1.4B](https://huggingface.co/bczhou/tiny-llava-v1-hf) is out!
-
-## &#x231B; TODO
-- [ ] Add support for Ollama and llama.cpp.
-- [x] Developers' guide / How to build demo locally.
-- [x] Training and custom finetuning docs.
-- [x] Model Zoo descriptions.
-- [x] Examples and inference.
-- [x] Release code for training.
-- [x] Add descriptions for evaluation.
-- [x] Add descriptions for data preparation.
-- [x] Release TinyLLaVA-1.5B and TinyLLaVA-2.0B.
-- [x] Release TinyLLaVA-3.1B.
-- [x] Release the evaluation code and weights today(2024.2.23).
-### &#x1F525; High performance, but with fewer parameters
-
-- Our best model, TinyLLaVA-3.1B, achieves better overall performance against existing 7B models such as LLaVA-1.5 and Qwen-VL.
-
-## Contents
-
-- [Install](#x1f527-requirements-and-installation)
-- [Model Zoo](#x1f433-model-zoo)
-- [Demo](#Demo)
-- [Quick Start](#x1f527-quick-start)
-- [Run Inference](#x1f527-run-inference)
-- [Evaluation](#evaluation)
-- [Data](#data-preparation)
-- [Train](#train)
-- [Custom Finetune](#custom-finetune)
-
-
-## &#x1F527; Requirements and Installation
-
-We recommend the requirements as follows.
-
-1. Clone this repository and navigate to LLaVA folder
-```bash
-git clone https://github.com/DLCV-BUAA/TinyLLaVABench.git
-cd TinyLLaVABench
-```
-
-2. Install Package
-```Shell
-conda create -n tinyllava python=3.10 -y
-conda activate tinyllava
-pip install --upgrade pip  # enable PEP 660 support
-pip install -e .
-```
-
-3. Install additional packages for training cases
-```Shell
-pip install -e ".[train]"
-pip install flash-attn --no-build-isolation
-```
-### Upgrade to the latest code base
-
-```Shell
-git pull
-pip install -e .
-
-# if you see some import errors when you upgrade, please try running the command below (without #)
-# pip install flash-attn --no-build-isolation --no-cache-dir
-```
-
-## &#x1F433; Model Zoo
-### Legacy Model
-- [tiny-llava-hf](https://huggingface.co/bczhou/tiny-llava-v1-hf)
-
-### Pretrained Models
-- [TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B)
-- [TinyLLaVA-2.0B](https://huggingface.co/bczhou/TinyLLaVA-2.0B)
-- [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B)
-
-### Model Details
-| Name          | LLM               | Checkpoint                                     | LLaVA-Bench-Wild | MME      | MMBench | MM-Vet | SQA-image | VQA-v2 | GQA   | TextVQA |
-|---------------|-------------------|------------------------------------------------|------------------|----------|---------|--------|-----------|--------|-------|---------|
-| TinyLLaVA-3.1B | Phi-2             | [TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B) | 75.8             | 1464.9   | 66.9    | 32.0   | 69.1      | 79.9   | 62.0  | 59.1    |
-| TinyLLaVA-2.0B | StableLM-2-1.6B   | [TinyLLaVA-2.0B](https://huggingface.co/bczhou/TinyLLaVA-2.0B) | 66.4             | 1433.8     | 63.3    | 32.6   | 64.7      | 78.9   | 61.9  | 56.4    |
-| TinyLLaVA-1.5B | TinyLlama         | [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B) | 60.8             | 1276.5     | 55.2     | 25.8   | 60.3      | 76.9   | 60.3  | 51.7    |
-
-
-## Demo
-
-### Gradio Web Demo
-
-Launch a local web demo by running:
-```shell
-python tinyllava/serve/app.py --model-path bczhou/TinyLLaVA-3.1B --model-name TinyLLaVA-3.1B
-```
-
-### CLI Inference
-
-We also support running inference with CLI. To use our model, run:
-```shell
-python -m tinyllava.serve.cli \
-    --model-path bczhou/TinyLLaVA-3.1B \
-    --image-file "./tinyllava/serve/examples/extreme_ironing.jpg" 
-```
-
-
-## &#x1F527; Quick Start
-
-<details>
-<summary>Load model</summary>
-    
-```Python
-from tinyllava.model.builder import load_pretrained_model
-from tinyllava.mm_utils import get_model_name_from_path
-from tinyllava.eval.run_tiny_llava import eval_model
-
-model_path = "bczhou/TinyLLaVA-3.1B"
-
-tokenizer, model, image_processor, context_len = load_pretrained_model(
-    model_path=model_path,
-    model_base=None,
-    model_name=get_model_name_from_path(model_path)
-)
-```
-</details>
-
-## &#x1F527; Run Inference
-Here's an example of running inference with [TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B)
-<details>
-<summary>Run Inference</summary>
-    
-```Python
-from tinyllava.model.builder import load_pretrained_model
-from tinyllava.mm_utils import get_model_name_from_path
-from tinyllava.eval.run_tiny_llava import eval_model
-
-model_path = "bczhou/TinyLLaVA-3.1B"
-prompt = "What are the things I should be cautious about when I visit here?"
-image_file = "https://llava-vl.github.io/static/images/view.jpg"
-
-args = type('Args', (), {
-    "model_path": model_path,
-    "model_base": None,
-    "model_name": get_model_name_from_path(model_path),
-    "query": prompt,
-    "conv_mode": "phi",
-    "image_file": image_file,
-    "sep": ",",
-    "temperature": 0,
-    "top_p": None,
-    "num_beams": 1,
-    "max_new_tokens": 512
-})()
-
-eval_model(args)
-```
-</details>
-
-### Important
-We use different `conv_mode` for different models. Replace the `conv_mode` in `args` according to this table:
-| model          	| conv_mode 	|
-|----------------	|-----------	|
-| TinyLLaVA-3.1B 	| phi       	|
-| TinyLLaVA-2.0B 	| phi       	|
-| TinyLLaVA-1.5B 	| v1        	|
-
-## Evaluation
-To ensure the reproducibility, we evaluate the models with greedy decoding.
-
-See [Evaluation.md](https://github.com/DLCV-BUAA/TinyLLaVABench/blob/main/docs/Evaluation.md)
-
-## Data Preparation
-
-In our paper, we used two different datasets: the [LLaVA dataset](https://github.com/haotian-liu/LLaVA?tab=readme-ov-file#pretrain-feature-alignment) and the [ShareGPT4V dataset](https://github.com/InternLM/InternLM-XComposer/blob/main/projects/ShareGPT4V/docs/Data.md), and compared their differences. In this section, we provide information on data preparation.
-
-### Pretraining Images
-* LLaVA: The pretraining images of LLaVA is from the 558K subset of the LAION-CC-SBU dataset.
-* ShareGPT4V: The pretraining images of ShareGPT4V is a mixture of 558K LAION-CC-SBU subset, SAM dataset, and COCO dataset.
-
-### Pretraining Annotations
-* LLaVA: The pretraining annotations of LLaVA are [here](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain).
-* ShareGPT4V: The pretraining annotations of ShareGPT4V are [here](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/share-captioner_coco_lcs_sam_1246k_1107.json).
-
-
-### SFT Images & Annotations
-The majority of the two SFT datasets are the same, with the exception that the 23K detailed description data in LLaVA-1.5-SFT being replaced with detailed captions randomly sampled from the [100K ShareGPT4V data](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/sharegpt4v_instruct_gpt4-vision_cap100k.json).
-
-### Download data
-
-1. Download relevant images
-
-- LAION-CC-SBU-558K: [images.zip](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain/blob/main/images.zip)
-- COCO: This dataset is from the [COCO2017 challenge](https://cocodataset.org/). Download: [train2017](http://images.cocodataset.org/zips/train2017.zip)
-- WebData: This dataset is curated by the [ShareGPT4V project](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V). Download: [images](https://drive.google.com/drive/folders/1tCUQ-sq6vdshZVkF0ZeF3K4eztkXJgax?usp=sharing). Only for academic usage.
-- SAM: This dataset is collected by [Meta](https://ai.meta.com/datasets/segment-anything-downloads/). Download: [images](https://ai.meta.com/datasets/segment-anything-downloads/). We only use 000000~000050.tar for now. If you just want to use ShareGPT4V for SFT, you can quickly download 9K images from [here](https://drive.google.com/file/d/1dKumdOKSXtV7lIXdrG7jsIK_z2vZv2gs/view?usp=drive_link).
-- GQA: [GQA project page](https://cs.stanford.edu/people/dorarad/gqa/about.html). Download: [images](https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip)
-- OCR-VQA: [OCR-VQA project page](https://ocr-vqa.github.io/). Download: [download script](https://drive.google.com/drive/folders/1_GYPY5UkUy7HIcR0zq3ZCFgeZN7BAfm_?usp=sharing). We save all files as `.jpg`
-- TextVQA: [TextVQA project page](https://textvqa.org/). Download: [trainvalimages](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip)
-- VisualGenome: [VisualGenome project page](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html). Download: [part1](https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip), [part2](https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip)
-
-
-2. Download relevant annotations
-
-- LLaVA's pretraining annotations: [blip_laion_cc_sbu_558k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain)
-- LLaVA's SFT annotations: [llava_v1_5_mix665k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json)
-- ShareGPT4V's pretraining annotations: [share-captioner_coco_lcs_sam_1246k_1107.json](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/share-captioner_coco_lcs_sam_1246k_1107.json)
-- ShareGPT4V's SFT annotations: [sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/blob/main/sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json)
-
-
-### Organize Data
-
-Organize the image files and annotation files as follows in `path/to/your/data`:
-
-```none
-data
-├── llava
-│   ├── llava_pretrain
-│   │   ├── images
-│   │   ├── blip_laion_cc_sbu_558k.json
-├── coco
-│   ├── train2017
-├── sam
-│   ├── images
-├── gqa
-│   ├── images
-├── ocr_vqa
-│   ├── images
-├── textvqa
-│   ├── train_images
-├── vg
-│   ├── VG_100K
-│   ├── VG_100K_2
-├── share_textvqa
-│   ├── images
-├── web-celebrity
-│   ├── images
-├── web-landmark
-│   ├── images
-├── wikiart
-│   ├── images
-├── text_files
-│   ├── llava_v1_5_mix665k.json
-│   ├── share-captioner_coco_lcs_sam_1246k_1107.json
-│   ├── sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json
-```
-
-## Train
-
-**This section we describe the base recipe.**
-### Hyperparameters
-Both hyperparameters used in pretraining and finetuning are provided below.
-
-1. Pretraining
-
-| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
-|----------------| ---: | ---: | ---: |-----------:| ---: |
-| TinyLLaVA-3.1B | 256 | 1e-3 | 1 |       3072 | 0 |
-
-2. Finetuning
-
-| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
-|----------------| ---: | ---: | ---: |-----------:| ---: |
-| TinyLLaVA-3.1B | 128 | 2e-5 | 1 |       3072 | 0 |
-
-### Pretrain
-
-**Replace paths to your paths**
-
-Training script with DeepSpeed ZeRO-2: [`pretrain.sh`](https://github.com/DLCV-BUAA/TinyLLaVABench/blob/main/scripts/tiny_llava/pretrain.sh).
-
-### Finetune
-
-**Replace paths to your paths**
-
-Training script with DeepSpeed ZeRO-3: [`finetune.sh`](https://github.com/DLCV-BUAA/TinyLLaVABench/blob/main/scripts/tiny_llava/finetune.sh).
-
-## Custom-Finetune
-
-Check out our custom finetune using LoRA [here](https://github.com/DLCV-BUAA/TinyLLaVABench/blob/dev/docs/CUTOM_FINETUNE.md).
-
-
-## &#x270F; Citation
-
-If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
-
-```BibTeX
-@misc{zhou2024tinyllava,
-      title={TinyLLaVA: A Framework of Small-scale Large Multimodal Models}, 
-      author={Baichuan Zhou and Ying Hu and Xi Weng and Junlong Jia and Jie Luo and Xien Liu and Ji Wu and Lei Huang},
-      year={2024},
-      eprint={2402.14289},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG}
-}
-```
-
-
-## ❤️ Community efforts
-* Our codebase is built upon the [LLaVA](https://github.com/haotian-liu/LLaVA) project. Great work!
-* Our project uses data from the [ShareGPT4V](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V) project. Great work!
-