bczhou
/

tiny-llava-v1-hf

@@ -1,43 +1,307 @@
 ---
 license: apache-2.0
 datasets:
 - liuhaotian/LLaVA-Pretrain
 - liuhaotian/LLaVA-Instruct-150K
 language:
 - en
 - zh
-library_name: transformers
 ---
-# WORK IN PROGRESS
-We present TinyLLaVA, a small vision-language chatbot (1.4B) that reaches comparable performances with contemporary vision language models on common benchmarks, using less parameters.
-TinyLLaVA was trained by finetuning [TinyLlama](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) on the [LLaVA-1.5](https://github.com/haotian-liu/LLaVA) dataset, following the training recipe of [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). For more details, please refer to the [LLaVA-1.5 paper](https://arxiv.org/abs/2310.03744).
-## Model Performance
-We have evaluated TinyLLaVA on [GQA](https://cs.stanford.edu/people/dorarad/gqa/about.html), [VizWiz](https://www.vizwiz.com/), [VQAv2](https://visualqa.org/), [TextVQA](https://textvqa.org/) and [SQA](https://github.com/lupantech/ScienceQA).
-|   Model   |     VQAv2      |      GQA       |       SQA      |      TextVQA   |      VizWiz    |
-| -------------------- | :------------: | :------------: | :------------: | :------------: | :------------: |
-|   TinyLLaVA-v1-tinyllama  |      73.41     |     57.54      |     59.40      |     46.37      |            |
-| TinyLLaVA-v1-stablelm |    74.9       |     58.86      |     62.82      |     49.52      |      35.6       |
-| TinyLLaVA-v1.1-tinyllama|    75.24    |     59.43      |     58.80      |     48.05      |      34.74      |
-| TinyLLaVA-v1.1-stablelm|    76.34     |     60.26      |     63.06      |      51.6      |     36.34       |
-|   BLIP-2             |      41.00     |     41.00      |     61.00      |     42.50      |      19.60      |
-|   LLaVA-v1.5-7B      |      78.50     |     62.00      |     66.80      |     61.3      |      50      |
-|   LLaVA-v1.5-13B     |      80.00     |     63.30      |     71.60      |     61.3      |      53.6      |
-|   Qwen-VL-7B         |      78.80     |     59.30      |     67.10      |     63.8      |      35.2      |
-|   Qwen-VL-13B        |      78.20     |     57.50      |     68.20      |     61.5      |      38.9      |
-More evaluations are ongoing.
-## Model Preparations
-#### - Transformers Version
-Make sure to have `transformers >= 4.35.3`.
 #### - Prompt Template
 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.
@@ -83,9 +347,6 @@ output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
 print(processor.decode(output[0][2:], skip_special_tokens=True))
 ```
-## Contact
-This model was trained by [Baichuan Zhou](https://baichuanzhou.github.io/), from Beihang Univerisity, under the supervision of  [Prof. Lei Huang](https://huangleibuaa.github.io/).
 ## &#x270F; Citation
 If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
@@ -99,4 +360,10 @@ If you find our paper and code useful in your research, please consider giving a
       archivePrefix={arXiv},
       primaryClass={cs.LG}
 }
-```

 ---
 license: apache-2.0
 datasets:
+- Lin-Chen/ShareGPT4V
 - liuhaotian/LLaVA-Pretrain
 - liuhaotian/LLaVA-Instruct-150K
 language:
 - en
 - zh
+tags:
+- llava
+- vision-language
+- llm
+- lmm
 ---
+<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)
+## &#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).
 #### - Prompt Template
 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.
 print(processor.decode(output[0][2:], skip_special_tokens=True))
 ```
 ## &#x270F; Citation
 If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
       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!