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Model: bczhou/tiny-llava-v1-hf
Source: Original Platform

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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) 
+
+## &#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.
+
+## Model Inference from `pipeline` and `transformers`
+#### - Using `pipeline`:
+Below we used [`"bczhou/tiny-llava-v1-hf"`](https://huggingface.co/bczhou/tiny-llava-v1-hf) checkpoint.
+
+```python
+from transformers import pipeline
+from PIL import Image
+import requests
+model_id = "bczhou/tiny-llava-v1-hf"
+pipe = pipeline("image-to-text", model=model_id)
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+prompt = "USER: <image>\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT:"
+outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
+print(outputs[0])
+>>> {"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."}
+```
+
+#### - Using pure `transformers`:
+Below is an example script to run generation in `float16` precision on a GPU device:
+
+```python
+import requests
+from PIL import Image
+import torch
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+model_id = "bczhou/tiny-llava-v1-hf"
+prompt = "USER: <image>\nWhat are these?\nASSISTANT:"
+image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
+model = LlavaForConditionalGeneration.from_pretrained(
+model_id,
+torch_dtype=torch.float16,
+low_cpu_mem_usage=True,
+).to(0)
+processor = AutoProcessor.from_pretrained(model_id)
+raw_image = Image.open(requests.get(image_file, stream=True).raw)
+inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
+output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
+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:.
+
+```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!

config.json

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+{
+  "architectures": [
+    "LlavaForConditionalGeneration"
+  ],
+  "ignore_index": -100,
+  "image_token_index": 32000,
+  "model_type": "llava",
+  "pad_token_id": 32001,
+  "projector_hidden_act": "gelu",
+  "text_config": {
+    "_name_or_path": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    "architectures": [
+      "LlamaForCausalLM"
+    ],
+    "hidden_size": 2048,
+    "intermediate_size": 5632,
+    "model_type": "llama",
+    "num_hidden_layers": 22,
+    "num_key_value_heads": 4,
+    "rms_norm_eps": 1e-05,
+    "torch_dtype": "bfloat16",
+    "vocab_size": 32064
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.2",
+  "vision_config": {
+    "hidden_size": 1024,
+    "image_size": 336,
+    "intermediate_size": 4096,
+    "model_type": "clip_vision_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "patch_size": 14,
+    "projection_dim": 768,
+    "vocab_size": 32000
+  },
+  "vision_feature_layer": -2,
+  "vision_feature_select_strategy": "default",
+  "vocab_size": 32064
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 32001,
+  "transformers_version": "4.36.2"
+}

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+}

preprocessor_config.json

@@ -0,0 +1,29 @@
+{
+  "crop_size": {
+    "height": 336,
+    "width": 336
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "processor_class": "LlavaProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 336
+  }
+}

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,59 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<image>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32001": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": "<pad>",
+  "padding_side": "right",
+  "processor_class": "LlavaProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}