369 lines
15 KiB
Markdown
369 lines
15 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- Lin-Chen/ShareGPT4V
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- liuhaotian/LLaVA-Pretrain
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- liuhaotian/LLaVA-Instruct-150K
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language:
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- en
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- zh
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tags:
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- llava
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- vision-language
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- llm
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- lmm
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pipeline_tag: image-text-to-text
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---
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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>
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<h5 align="center">
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[](https://github.com/DLCV-BUAA/TinyLLaVABench) [](https://arxiv.org/abs/2402.14289) [](https://github.com/PKU-YuanGroup/MoE-LLaVA/blob/main/LICENSE)
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## 🎉 News
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* **[2024.03.10]** base recipe out!
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* **[2024.03.10]** Finetune scripts out!
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* **[2024.02.25]** Update evaluation scripts and docs!
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* **[2024.02.25]** Data descriptions out. Release TinyLLaVA-1.5B and TinyLLaVA-2.0B!
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* **[2024.02.24]** Example code on inference and model loading added!
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* **[2024.02.23]** Evaluation code and scripts released!
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* **[2024.02.21]** Creating the [TinyLLaVABench](https://github.com/DLCV-BUAA/TinyLLavaBench) repository on GitHub!
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* **[2024.02.21]** Our paper: [TinyLLaVA: A Framework of Small-scale Large Multimodal Models](https://arxiv.org/abs/2402.14289) is out!
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* **[2024.01.11]** Our fist model [TinyLLaVA-1.4B](https://huggingface.co/bczhou/tiny-llava-v1-hf) is out!
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## ⌛ TODO
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- [ ] Add support for Ollama and llama.cpp.
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- [x] Developers' guide / How to build demo locally.
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- [x] Training and custom finetuning docs.
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- [x] Model Zoo descriptions.
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- [x] Examples and inference.
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- [x] Release code for training.
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- [x] Add descriptions for evaluation.
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- [x] Add descriptions for data preparation.
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- [x] Release TinyLLaVA-1.5B and TinyLLaVA-2.0B.
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- [x] Release TinyLLaVA-3.1B.
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- [x] Release the evaluation code and weights today(2024.2.23).
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### 🔥 High performance, but with fewer parameters
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- Our best model, TinyLLaVA-3.1B, achieves better overall performance against existing 7B models such as LLaVA-1.5 and Qwen-VL.
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## Contents
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- [Install](#x1f527-requirements-and-installation)
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- [Model Zoo](#x1f433-model-zoo)
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- [Demo](#Demo)
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- [Quick Start](#x1f527-quick-start)
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- [Run Inference](#x1f527-run-inference)
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- [Evaluation](#evaluation)
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- [Data](#data-preparation)
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- [Train](#train)
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- [Custom Finetune](#custom-finetune)
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## 🔧 Requirements and Installation
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We recommend the requirements as follows.
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1. Clone this repository and navigate to LLaVA folder
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```bash
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git clone https://github.com/DLCV-BUAA/TinyLLaVABench.git
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cd TinyLLaVABench
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```
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2. Install Package
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```Shell
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conda create -n tinyllava python=3.10 -y
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conda activate tinyllava
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pip install --upgrade pip # enable PEP 660 support
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pip install -e .
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```
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3. Install additional packages for training cases
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```Shell
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pip install -e ".[train]"
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pip install flash-attn --no-build-isolation
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```
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### Upgrade to the latest code base
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```Shell
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git pull
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pip install -e .
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# if you see some import errors when you upgrade, please try running the command below (without #)
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# pip install flash-attn --no-build-isolation --no-cache-dir
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```
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## 🐳 Model Zoo
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### Legacy Model
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- [tiny-llava-hf](https://huggingface.co/bczhou/tiny-llava-v1-hf)
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### Pretrained Models
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- [TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B)
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- [TinyLLaVA-2.0B](https://huggingface.co/bczhou/TinyLLaVA-2.0B)
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- [TinyLLaVA-1.5B](https://huggingface.co/bczhou/TinyLLaVA-1.5B)
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### Model Details
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| Name | LLM | Checkpoint | LLaVA-Bench-Wild | MME | MMBench | MM-Vet | SQA-image | VQA-v2 | GQA | TextVQA |
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|---------------|-------------------|------------------------------------------------|------------------|----------|---------|--------|-----------|--------|-------|---------|
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| 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 |
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| 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 |
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| 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 |
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## Demo
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### Gradio Web Demo
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Launch a local web demo by running:
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```shell
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python tinyllava/serve/app.py --model-path bczhou/TinyLLaVA-3.1B --model-name TinyLLaVA-3.1B
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```
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### CLI Inference
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We also support running inference with CLI. To use our model, run:
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```shell
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python -m tinyllava.serve.cli \
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--model-path bczhou/TinyLLaVA-3.1B \
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--image-file "./tinyllava/serve/examples/extreme_ironing.jpg"
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```
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## 🔧 Quick Start
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<details>
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<summary>Load model</summary>
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```Python
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from tinyllava.model.builder import load_pretrained_model
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from tinyllava.mm_utils import get_model_name_from_path
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from tinyllava.eval.run_tiny_llava import eval_model
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model_path = "bczhou/TinyLLaVA-3.1B"
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tokenizer, model, image_processor, context_len = load_pretrained_model(
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model_path=model_path,
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model_base=None,
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model_name=get_model_name_from_path(model_path)
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)
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```
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</details>
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## 🔧 Run Inference
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Here's an example of running inference with [TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B)
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<details>
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<summary>Run Inference</summary>
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```Python
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from tinyllava.model.builder import load_pretrained_model
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from tinyllava.mm_utils import get_model_name_from_path
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from tinyllava.eval.run_tiny_llava import eval_model
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model_path = "bczhou/TinyLLaVA-3.1B"
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prompt = "What are the things I should be cautious about when I visit here?"
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image_file = "https://llava-vl.github.io/static/images/view.jpg"
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args = type('Args', (), {
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"model_path": model_path,
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"model_base": None,
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"model_name": get_model_name_from_path(model_path),
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"query": prompt,
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"conv_mode": "phi",
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"image_file": image_file,
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"sep": ",",
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"temperature": 0,
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"top_p": None,
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"num_beams": 1,
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"max_new_tokens": 512
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})()
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eval_model(args)
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```
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</details>
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### Important
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We use different `conv_mode` for different models. Replace the `conv_mode` in `args` according to this table:
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| model | conv_mode |
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|---------------- |----------- |
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| TinyLLaVA-3.1B | phi |
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| TinyLLaVA-2.0B | phi |
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| TinyLLaVA-1.5B | v1 |
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## Evaluation
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To ensure the reproducibility, we evaluate the models with greedy decoding.
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See [Evaluation.md](https://github.com/DLCV-BUAA/TinyLLaVABench/blob/main/docs/Evaluation.md)
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## Data Preparation
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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.
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### Pretraining Images
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* LLaVA: The pretraining images of LLaVA is from the 558K subset of the LAION-CC-SBU dataset.
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* ShareGPT4V: The pretraining images of ShareGPT4V is a mixture of 558K LAION-CC-SBU subset, SAM dataset, and COCO dataset.
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### Pretraining Annotations
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* LLaVA: The pretraining annotations of LLaVA are [here](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain).
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* 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).
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### SFT Images & Annotations
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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).
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### Download data
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1. Download relevant images
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- LAION-CC-SBU-558K: [images.zip](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain/blob/main/images.zip)
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- COCO: This dataset is from the [COCO2017 challenge](https://cocodataset.org/). Download: [train2017](http://images.cocodataset.org/zips/train2017.zip)
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- 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.
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- 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).
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- 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)
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- 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`
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- TextVQA: [TextVQA project page](https://textvqa.org/). Download: [trainvalimages](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip)
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- 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)
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2. Download relevant annotations
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- LLaVA's pretraining annotations: [blip_laion_cc_sbu_558k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain)
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- LLaVA's SFT annotations: [llava_v1_5_mix665k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json)
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- 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)
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- 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)
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### Organize Data
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Organize the image files and annotation files as follows in `path/to/your/data`:
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```none
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data
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├── llava
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│ ├── llava_pretrain
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│ │ ├── images
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│ │ ├── blip_laion_cc_sbu_558k.json
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├── coco
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│ ├── train2017
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├── sam
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│ ├── images
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├── gqa
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│ ├── images
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├── ocr_vqa
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│ ├── images
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├── textvqa
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│ ├── train_images
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├── vg
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│ ├── VG_100K
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│ ├── VG_100K_2
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├── share_textvqa
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│ ├── images
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├── web-celebrity
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│ ├── images
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├── web-landmark
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│ ├── images
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├── wikiart
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│ ├── images
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├── text_files
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│ ├── llava_v1_5_mix665k.json
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│ ├── share-captioner_coco_lcs_sam_1246k_1107.json
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│ ├── sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json
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```
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## Train
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**This section we describe the base recipe.**
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### Hyperparameters
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Both hyperparameters used in pretraining and finetuning are provided below.
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1. Pretraining
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| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
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|----------------| ---: | ---: | ---: |-----------:| ---: |
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| TinyLLaVA-3.1B | 256 | 1e-3 | 1 | 3072 | 0 |
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2. Finetuning
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| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
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|----------------| ---: | ---: | ---: |-----------:| ---: |
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| TinyLLaVA-3.1B | 128 | 2e-5 | 1 | 3072 | 0 |
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### Pretrain
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**Replace paths to your paths**
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Training script with DeepSpeed ZeRO-2: [`pretrain.sh`](https://github.com/DLCV-BUAA/TinyLLaVABench/blob/main/scripts/tiny_llava/pretrain.sh).
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### Finetune
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**Replace paths to your paths**
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Training script with DeepSpeed ZeRO-3: [`finetune.sh`](https://github.com/DLCV-BUAA/TinyLLaVABench/blob/main/scripts/tiny_llava/finetune.sh).
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## Custom-Finetune
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Check out our custom finetune using LoRA [here](https://github.com/DLCV-BUAA/TinyLLaVABench/blob/dev/docs/CUTOM_FINETUNE.md).
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#### - Prompt Template
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The model supports multi-image and multi-prompt generation. When using the model, make sure to follow the correct prompt template (`USER: <image>xxx\nASSISTANT:`), where `<image>` token is a place-holding special token for image embeddings.
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## Model Inference from `pipeline` and `transformers`
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#### - Using `pipeline`:
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Below we used [`"bczhou/tiny-llava-v1-hf"`](https://huggingface.co/bczhou/tiny-llava-v1-hf) checkpoint.
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```python
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from transformers import pipeline
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from PIL import Image
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import requests
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model_id = "bczhou/tiny-llava-v1-hf"
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pipe = pipeline("image-to-text", model=model_id)
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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prompt = "USER: <image>\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT:"
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outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
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print(outputs[0])
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>>> {"generated_text': 'USER: \nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT: The label 15 represents lava, which is a type of volcanic rock."}
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```
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#### - Using pure `transformers`:
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Below is an example script to run generation in `float16` precision on a GPU device:
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```python
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import requests
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from PIL import Image
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import torch
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from transformers import AutoProcessor, LlavaForConditionalGeneration
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model_id = "bczhou/tiny-llava-v1-hf"
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prompt = "USER: <image>\nWhat are these?\nASSISTANT:"
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image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
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model = LlavaForConditionalGeneration.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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low_cpu_mem_usage=True,
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).to(0)
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processor = AutoProcessor.from_pretrained(model_id)
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raw_image = Image.open(requests.get(image_file, stream=True).raw)
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inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
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output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
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print(processor.decode(output[0][2:], skip_special_tokens=True))
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```
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## ✏ Citation
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If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
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```BibTeX
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@misc{zhou2024tinyllava,
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title={TinyLLaVA: A Framework of Small-scale Large Multimodal Models},
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author={Baichuan Zhou and Ying Hu and Xi Weng and Junlong Jia and Jie Luo and Xien Liu and Ji Wu and Lei Huang},
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year={2024},
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eprint={2402.14289},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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```
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## ❤️ Community efforts
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* Our codebase is built upon the [LLaVA](https://github.com/haotian-liu/LLaVA) project. Great work!
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* Our project uses data from the [ShareGPT4V](https://github.com/InternLM/InternLM-XComposer/tree/main/projects/ShareGPT4V) project. Great work! |