TinyLLaVA-2.0B / README.md

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	---
	license: apache-2.0
	datasets:
	- Lin-Chen/ShareGPT4V
	- liuhaotian/LLaVA-Pretrain
	- liuhaotian/LLaVA-Instruct-150K
	language:
	- en
	- zh
	tags:
	- llava
	- vision-language
	- llm
	- lmm
	pipeline_tag: image-text-to-text
	---
	<h2 align="center"> <a href="https://arxiv.org/abs/2402.14289">TinyLLaVA: A Framework of Small-scale Large Multimodal Models</a>

	<h5 align="center">

	[![github](https://img.shields.io/badge/GitHub-TinyLLaVA-blue)](https://github.com/DLCV-BUAA/TinyLLaVABench) [![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)


	## 🎉 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!

	## ⌛ 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).
	### 🔥 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)


	## 🔧 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
	```

	## 🐳 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"
	```


	## 🔧 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>

	## 🔧 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).


	## ✏ 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!