Files

.devcontainer/Dockerfile

@@ -0,0 +1,53 @@
+FROM mcr.microsoft.com/devcontainers/base:ubuntu-20.04
+
+SHELL [ "bash", "-c" ]
+
+# update apt and install packages
+RUN apt update && \
+    apt install -yq \
+        ffmpeg \
+        dkms \
+        build-essential
+
+# add user tools
+RUN sudo apt install -yq \
+        jq \
+        jp \
+        tree \
+        tldr
+
+# add git-lfs and install
+RUN curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash && \
+    sudo apt-get install -yq git-lfs && \
+    git lfs install
+
+############################################
+# Setup user
+############################################
+
+USER vscode
+
+# install azcopy, a tool to copy to/from blob storage
+# for more info: https://learn.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-blobs-upload#upload-a-file
+RUN cd /tmp && \
+    wget https://azcopyvnext.azureedge.net/release20230123/azcopy_linux_amd64_10.17.0.tar.gz && \
+    tar xvf azcopy_linux_amd64_10.17.0.tar.gz && \
+    mkdir -p ~/.local/bin && \
+    mv azcopy_linux_amd64_10.17.0/azcopy ~/.local/bin && \
+    chmod +x ~/.local/bin/azcopy && \
+    rm -rf azcopy_linux_amd64*
+
+# Setup conda
+RUN cd /tmp && \
+    wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
+    bash ./Miniconda3-latest-Linux-x86_64.sh -b && \
+    rm ./Miniconda3-latest-Linux-x86_64.sh
+
+# Install dotnet
+RUN cd /tmp && \
+    wget https://dot.net/v1/dotnet-install.sh && \
+    chmod +x dotnet-install.sh && \
+    ./dotnet-install.sh --channel 7.0 && \
+    ./dotnet-install.sh --channel 3.1 && \
+    rm ./dotnet-install.sh
+

.devcontainer/devcontainer.env

@@ -0,0 +1,2 @@
+SAMPLE_ENV_VAR1="Sample Value"
+SAMPLE_ENV_VAR2=332431bf-68bf

.devcontainer/devcontainer.json

@@ -0,0 +1,71 @@
+{
+    "name": "LLaVA",
+    "build": {
+        "dockerfile": "Dockerfile",
+        "context": "..",
+        "args": {}
+    },
+    "features": {
+        "ghcr.io/devcontainers/features/docker-in-docker:2": {},
+        "ghcr.io/devcontainers/features/azure-cli:1": {},
+        "ghcr.io/azure/azure-dev/azd:0": {},
+        "ghcr.io/devcontainers/features/powershell:1": {},
+        "ghcr.io/devcontainers/features/common-utils:2": {},
+        "ghcr.io/devcontainers-contrib/features/zsh-plugins:0": {},
+    },
+    // "forwardPorts": [],
+    "postCreateCommand": "bash ./.devcontainer/postCreateCommand.sh",
+    "customizations": {
+        "vscode": {
+            "settings": {
+                "python.analysis.autoImportCompletions": true,
+                "python.analysis.autoImportUserSymbols": true,
+                "python.defaultInterpreterPath": "~/miniconda3/envs/llava/bin/python",
+                "python.formatting.provider": "yapf",
+                "python.linting.enabled": true,
+                "python.linting.flake8Enabled": true,
+                "isort.check": true,
+                "dev.containers.copyGitConfig": true,
+                "terminal.integrated.defaultProfile.linux": "zsh",
+                "terminal.integrated.profiles.linux": {
+                    "zsh": {
+                        "path": "/usr/bin/zsh"
+                    },
+                }
+            },
+            "extensions": [
+                "aaron-bond.better-comments",
+                "eamodio.gitlens",
+                "EditorConfig.EditorConfig",
+                "foxundermoon.shell-format",
+                "GitHub.copilot-chat",
+                "GitHub.copilot-labs",
+                "GitHub.copilot",
+                "lehoanganh298.json-lines-viewer",
+                "mhutchie.git-graph",
+                "ms-azuretools.vscode-docker",
+                "ms-dotnettools.dotnet-interactive-vscode",
+                "ms-python.flake8",
+                "ms-python.isort",
+                "ms-python.python",
+                "ms-python.vscode-pylance",
+                "njpwerner.autodocstring",
+                "redhat.vscode-yaml",
+                "stkb.rewrap",
+                "yzhang.markdown-all-in-one",
+            ]
+        }
+    },
+    "mounts": [],
+    "runArgs": [
+        "--gpus",
+        "all",
+        // "--ipc",
+        // "host",
+        "--ulimit",
+        "memlock=-1",
+        "--env-file",
+        ".devcontainer/devcontainer.env"
+    ],
+    // "remoteUser": "root"
+}

.devcontainer/postCreateCommand.sh

@@ -0,0 +1,45 @@
+git config --global safe.directory '*'
+git config --global core.editor "code --wait"
+git config --global pager.branch false
+
+# Set AZCOPY concurrency to auto
+echo "export AZCOPY_CONCURRENCY_VALUE=AUTO" >> ~/.zshrc
+echo "export AZCOPY_CONCURRENCY_VALUE=AUTO" >> ~/.bashrc
+
+# Activate conda by default
+echo ". /home/vscode/miniconda3/bin/activate" >> ~/.zshrc
+echo ". /home/vscode/miniconda3/bin/activate" >> ~/.bashrc
+
+# Use llava environment by default
+echo "conda activate llava" >> ~/.zshrc
+echo "conda activate llava" >> ~/.bashrc
+
+# Add dotnet to PATH
+echo 'export PATH="$PATH:$HOME/.dotnet"' >> ~/.bashrc
+echo 'export PATH="$PATH:$HOME/.dotnet"' >> ~/.zshrc
+
+# Create and activate llava environment
+source /home/vscode/miniconda3/bin/activate
+conda create -y -q -n llava python=3.10
+conda activate llava
+
+# Install Nvidia Cuda Compiler
+conda install -y -c nvidia cuda-compiler
+
+pip install pre-commit==3.0.2
+
+# Install package locally
+pip install --upgrade pip  # enable PEP 660 support
+pip install -e .
+
+# Install additional packages for training
+pip install -e ".[train]"
+pip install flash-attn --no-build-isolation
+
+# Download checkpoints to location outside of the repo
+git clone https://huggingface.co/liuhaotian/llava-v1.5-7b ~/llava-v1.5-7b
+
+# Commented because it is unlikely for users to have enough local GPU memory to load the model
+# git clone https://huggingface.co/liuhaotian/llava-v1.5-13b ~/llava-v1.5-13b
+
+echo "postCreateCommand.sh COMPLETE!"

.dockerignore

@@ -0,0 +1,21 @@
+# The .dockerignore file excludes files from the container build process.
+#
+# https://docs.docker.com/engine/reference/builder/#dockerignore-file
+
+# Exclude Git files
+.git
+.github
+.gitignore
+
+# Exclude Python cache files
+__pycache__
+.mypy_cache
+.pytest_cache
+.ruff_cache
+
+# Exclude Python virtual environment
+/venv
+
+# Exclude some weights
+/openai
+/liuhaotian

.editorconfig

@@ -0,0 +1,18 @@
+root = true
+
+# Unix-style newlines with a newline ending every file
+[*]
+end_of_line = lf
+insert_final_newline = true
+trim_trailing_whitespace = true
+charset = utf-8
+
+# 4 space indentation
+[*.{py,json}]
+indent_style = space
+indent_size = 4
+
+# 2 space indentation
+[*.{md,sh,yaml,yml}]
+indent_style = space
+indent_size = 2

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+images/demo_cli.gif filter=lfs diff=lfs merge=lfs -text

.github/ISSUE_TEMPLATE/1-usage.yaml

@@ -0,0 +1,31 @@
+name: Usage issues
+description: Report issues in usage.
+title: "[Usage] "
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this form.  Please give as detailed description as possible for us to better assist with the issue :)
+  - type: textarea
+    id: what-happened
+    attributes:
+      label: Describe the issue
+      description: Please give as detailed description as possible for us to better assist with the issue.  Please paste the **FULL** error log here, so that we can better understand the issue. Wrap the log with ``` for better readability in GitHub.
+      placeholder: Issue
+      value: |
+        Issue:
+        
+        Command:
+        ```
+        PASTE THE COMMANDS HERE.
+        ```
+        
+        Log: 
+        ```
+        PASTE THE LOGS HERE.
+        ```
+        
+        Screenshots:
+        You may attach screenshots if it better explains the issue.
+    validations:
+      required: true

.github/ISSUE_TEMPLATE/2-feature-request.yaml

@@ -0,0 +1,13 @@
+name: Feature Request
+description: Request for a new feature
+title: "[Feature request] "
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for your interest in our work.  Please share your thoughts of the new features below.
+  - type: textarea
+    id: feature
+    attributes:
+      label: feature
+      placeholder: Start your thoughts here...

.github/ISSUE_TEMPLATE/3-question.yaml

@@ -0,0 +1,13 @@
+name: Questions
+description: General questions about the work
+title: "[Question] "
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for your interest in our work.  For this type of question, it may be more suitable to go to [discussion](https://github.com/haotian-liu/LLaVA/discussions) sections.  If you believe an issue would be better for your request, please continue your post below :)
+  - type: textarea
+    id: question
+    attributes:
+      label: Question
+      placeholder: Start question here...

.github/ISSUE_TEMPLATE/4-discussion.yaml

@@ -0,0 +1,13 @@
+name: Discussions
+description: General discussions about the work
+title: "[Discussion] "
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for your interest in our work.  For this type of question, it may be more suitable to go to [discussion](https://github.com/haotian-liu/LLaVA/discussions) sections.  If you believe an issue would be better for your request, please continue your post below :)
+  - type: textarea
+    id: discussion
+    attributes:
+      label: Discussion
+      placeholder: Start discussion here...

.gitignore

@@ -0,0 +1,35 @@
+# Python
+__pycache__
+*.pyc
+*.egg-info
+dist
+
+# Log
+*.log
+*.log.*
+*.json
+*.jsonl
+
+# Data
+!**/alpaca-data-conversation.json
+
+# Editor
+.idea
+*.swp
+
+# Other
+.DS_Store
+wandb
+output
+
+checkpoints
+ckpts*
+
+.ipynb_checkpoints
+*.ipynb
+
+# DevContainer
+!.devcontainer/*
+
+# Demo
+serve_images/

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "git.ignoreLimitWarning": true
+}

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,7 +1,7 @@
 ---
 title: Fluffyvision
-emoji: 📈
-colorFrom: yellow
+emoji: 👁️
+colorFrom: blue
 colorTo: gray
 sdk: gradio
 sdk_version: 4.21.0

cog.yaml

@@ -0,0 +1,37 @@
+# Configuration for Cog ⚙️
+# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md
+
+build:
+  gpu: true
+
+  python_version: "3.11"
+
+  python_packages:
+    - "torch==2.0.1"
+    - "accelerate==0.21.0"
+    - "bitsandbytes==0.41.0"
+    - "deepspeed==0.9.5"
+    - "einops-exts==0.0.4"
+    - "einops==0.6.1"
+    - "gradio==3.35.2"
+    - "gradio_client==0.2.9"
+    - "httpx==0.24.0"
+    - "markdown2==2.4.10"
+    - "numpy==1.26.0"
+    - "peft==0.4.0"
+    - "scikit-learn==1.2.2"
+    - "sentencepiece==0.1.99"
+    - "shortuuid==1.0.11"
+    - "timm==0.6.13"
+    - "tokenizers==0.13.3"
+    - "torch==2.0.1"
+    - "torchvision==0.15.2"
+    - "transformers==4.31.0"
+    - "wandb==0.15.12"
+    - "wavedrom==2.0.3.post3"
+    - "Pygments==2.16.1"
+  run:
+    - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.0.3/pget" && chmod +x /usr/local/bin/pget
+
+# predict.py defines how predictions are run on your model
+predict: "predict.py:Predictor"

docs/Customize_Component.md

@@ -0,0 +1,20 @@
+# Customize Components in LLaVA
+
+This is an initial guide on how to replace the LLMs, visual encoders, etc. with your choice of components.
+
+## LLM
+
+It is quite simple to swap out LLaMA to any other LLMs.  You can refer to our implementation of [`llava_llama.py`](https://raw.githubusercontent.com/haotian-liu/LLaVA/main/llava/model/language_model/llava_llama.py) for an example of how to replace the LLM.
+
+Although it may seem that it still needs ~100 lines of code, most of them are copied from the original `llama.py` from HF.  The only part that is different is to insert some lines for processing the multimodal inputs.
+
+In `forward` function, you can see that we call `self.prepare_inputs_labels_for_multimodal` to process the multimodal inputs.  This function is defined in `LlavaMetaForCausalLM` and you just need to insert it into the `forward` function of your LLM.
+
+In `prepare_inputs_for_generation` function, you can see that we add `images` to the `model_inputs`.  This is because we need to pass the images to the LLM during generation.
+
+These are basically all the changes you need to make to replace the LLM.
+
+## Visual Encoder
+
+You can check out [`clip_encoder.py`](https://github.com/haotian-liu/LLaVA/blob/main/llava/model/multimodal_encoder/clip_encoder.py) on how we implement the CLIP visual encoder.
+

docs/Data.md

@@ -0,0 +1,29 @@
+## Data
+
+| Data file name | Size |
+| --- | ---: |
+| [llava_instruct_150k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_instruct_150k.json) | 229 MB |
+| [llava_instruct_80k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_instruct_80k.json) | 229 MB |
+| [conversation_58k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/conversation_58k.json) | 126 MB |
+| [detail_23k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/detail_23k.json) | 20.5 MB |
+| [complex_reasoning_77k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/complex_reasoning_77k.json) | 79.6 MB |
+
+### Pretraining Dataset
+The pretraining dataset used in this release is a subset of CC-3M dataset, filtered with a more balanced concept coverage distribution.  Please see [here](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K) for a detailed description of the dataset structure and how to download the images.
+
+If you already have CC-3M dataset on your disk, the image names follow this format: `GCC_train_000000000.jpg`.  You may edit the `image` field correspondingly if necessary.
+
+| Data | Chat File | Meta Data | Size |
+| --- |  --- |  --- | ---: |
+| CC-3M Concept-balanced 595K | [chat.json](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K/blob/main/chat.json) | [metadata.json](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K/blob/main/metadata.json) | 211 MB
+| LAION/CC/SBU BLIP-Caption Concept-balanced 558K | [blip_laion_cc_sbu_558k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain/blob/main/blip_laion_cc_sbu_558k.json) | [metadata.json](#) | 181 MB
+
+**Important notice**: Upon the request from the community, as ~15% images of the original CC-3M dataset are no longer accessible, we upload [`images.zip`](https://huggingface.co/datasets/liuhaotian/LLaVA-CC3M-Pretrain-595K/blob/main/images.zip) for better reproducing our work in research community. It must not be used for any other purposes. The use of these images must comply with the CC-3M license. This may be taken down at any time when requested by the original CC-3M dataset owner or owners of the referenced images.
+
+### GPT-4 Prompts
+
+We provide our prompts and few-shot samples for GPT-4 queries, to better facilitate research in this domain.  Please check out the [`prompts`](https://github.com/haotian-liu/LLaVA/tree/main/playground/data/prompts) folder for three kinds of questions: conversation, detail description, and complex reasoning.
+
+They are organized in a format of `system_message.txt` for system message, pairs of `abc_caps.txt` for few-shot sample user input, and `abc_conv.txt` for few-shot sample reference output.
+
+Note that you may find them in different format. For example, `conversation` is in `jsonl`, and detail description is answer-only.  The selected format in our preliminary experiments works slightly better than a limited set of alternatives that we tried: `jsonl`, more natural format, answer-only.  If interested, you may try other variants or conduct more careful study in this.  Contributions are welcomed!

docs/Evaluation.md

@@ -0,0 +1,167 @@
+# Evaluation
+
+In LLaVA-1.5, we evaluate models on a diverse set of 12 benchmarks. To ensure the reproducibility, we evaluate the models with greedy decoding. We do not evaluate using beam search to make the inference process consistent with the chat demo of real-time outputs.
+
+Currently, we mostly utilize the official toolkit or server for the evaluation.
+
+## Evaluate on Custom Datasets
+
+You can evaluate LLaVA on your custom datasets by converting your dataset to LLaVA's jsonl format, and evaluate using [`model_vqa.py`](https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/model_vqa.py).
+
+Below we provide a general guideline for evaluating datasets with some common formats.
+
+1. Short-answer (e.g. VQAv2, MME).
+
+```
+<question>
+Answer the question using a single word or phrase.
+```
+
+2. Option-only for multiple-choice (e.g. MMBench, SEED-Bench).
+
+```
+<question>
+A. <option_1>
+B. <option_2>
+C. <option_3>
+D. <option_4>
+Answer with the option's letter from the given choices directly.
+```
+
+3. Natural QA (e.g. LLaVA-Bench, MM-Vet).
+
+No postprocessing is needed.
+
+## Scripts
+
+Before preparing task-specific data, **you MUST first download [eval.zip](https://drive.google.com/file/d/1atZSBBrAX54yYpxtVVW33zFvcnaHeFPy/view?usp=sharing)**. It contains custom annotations, scripts, and the prediction files with LLaVA v1.5. Extract to `./playground/data/eval`. This also provides a general structure for all datasets.
+
+### VQAv2
+
+1. Download [`test2015`](http://images.cocodataset.org/zips/test2015.zip) and put it under `./playground/data/eval/vqav2`.
+2. Multi-GPU inference.
+```Shell
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1_5/eval/vqav2.sh
+```
+3. Submit the results to the [evaluation server](https://eval.ai/web/challenges/challenge-page/830/my-submission): `./playground/data/eval/vqav2/answers_upload`.
+
+### GQA
+
+1. Download the [data](https://cs.stanford.edu/people/dorarad/gqa/download.html) and [evaluation scripts](https://cs.stanford.edu/people/dorarad/gqa/evaluate.html) following the official instructions and put under `./playground/data/eval/gqa/data`. You may need to modify `eval.py` as [this](https://gist.github.com/haotian-liu/db6eddc2a984b4cbcc8a7f26fd523187) due to the missing assets in the GQA v1.2 release.
+2. Multi-GPU inference.
+```Shell
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1_5/eval/gqa.sh
+```
+
+### VisWiz
+
+1. Download [`test.json`](https://vizwiz.cs.colorado.edu/VizWiz_final/vqa_data/Annotations.zip) and extract [`test.zip`](https://vizwiz.cs.colorado.edu/VizWiz_final/images/test.zip) to `test`. Put them under `./playground/data/eval/vizwiz`.
+2. Single-GPU inference.
+```Shell
+CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/vizwiz.sh
+```
+3. Submit the results to the [evaluation server](https://eval.ai/web/challenges/challenge-page/1911/my-submission): `./playground/data/eval/vizwiz/answers_upload`.
+
+### ScienceQA
+
+1. Under `./playground/data/eval/scienceqa`, download `images`, `pid_splits.json`, `problems.json` from the `data/scienceqa` folder of the ScienceQA [repo](https://github.com/lupantech/ScienceQA).
+2. Single-GPU inference and evaluate.
+```Shell
+CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/sqa.sh
+```
+
+### TextVQA
+
+1. Download [`TextVQA_0.5.1_val.json`](https://dl.fbaipublicfiles.com/textvqa/data/TextVQA_0.5.1_val.json) and [images](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip) and extract to `./playground/data/eval/textvqa`.
+2. Single-GPU inference and evaluate.
+```Shell
+CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/textvqa.sh
+```
+
+### POPE
+
+1. Download `coco` from [POPE](https://github.com/AoiDragon/POPE/tree/e3e39262c85a6a83f26cf5094022a782cb0df58d/output/coco) and put under `./playground/data/eval/pope`.
+2. Single-GPU inference and evaluate.
+```Shell
+CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/pope.sh
+```
+
+### MME
+
+1. Download the data following the official instructions [here](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation).
+2. Downloaded images to `MME_Benchmark_release_version`.
+3. put the official `eval_tool` and `MME_Benchmark_release_version` under `./playground/data/eval/MME`.
+4. Single-GPU inference and evaluate.
+```Shell
+CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mme.sh
+```
+
+### MMBench
+
+1. Download [`mmbench_dev_20230712.tsv`](https://download.openmmlab.com/mmclassification/datasets/mmbench/mmbench_dev_20230712.tsv) and put under `./playground/data/eval/mmbench`.
+2. Single-GPU inference.
+```Shell
+CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mmbench.sh
+```
+3. Submit the results to the [evaluation server](https://opencompass.org.cn/leaderboard-multimodal): `./playground/data/eval/mmbench/answers_upload/mmbench_dev_20230712`.
+
+### MMBench-CN
+
+1. Download [`mmbench_dev_cn_20231003.tsv`](https://download.openmmlab.com/mmclassification/datasets/mmbench/mmbench_dev_cn_20231003.tsv) and put under `./playground/data/eval/mmbench`.
+2. Single-GPU inference.
+```Shell
+CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mmbench_cn.sh
+```
+3. Submit the results to the evaluation server: `./playground/data/eval/mmbench/answers_upload/mmbench_dev_cn_20231003`.
+
+
+### SEED-Bench
+
+1. Following the official [instructions](https://github.com/AILab-CVC/SEED-Bench/blob/main/DATASET.md) to download the images and the videos. Put images under `./playground/data/eval/seed_bench/SEED-Bench-image`.
+2. Extract the video frame in the middle from the downloaded videos, and put them under `./playground/data/eval/seed_bench/SEED-Bench-video-image`. We provide our script `extract_video_frames.py` modified from the official one.
+3. Multiple-GPU inference and evaluate.
+```Shell
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/v1_5/eval/seed.sh
+```
+4. Optionally, submit the results to the leaderboard: `./playground/data/eval/seed_bench/answers_upload` using the official jupyter notebook.
+
+### LLaVA-Bench-in-the-Wild
+
+1. Extract contents of [`llava-bench-in-the-wild`](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild) to `./playground/data/eval/llava-bench-in-the-wild`.
+2. Single-GPU inference and evaluate.
+```Shell
+CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/llavabench.sh
+```
+
+### MM-Vet
+
+1. Extract [`mm-vet.zip`](https://github.com/yuweihao/MM-Vet/releases/download/v1/mm-vet.zip) to `./playground/data/eval/mmvet`.
+2. Single-GPU inference.
+```Shell
+CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/mmvet.sh
+```
+3. Evaluate the predictions in `./playground/data/eval/mmvet/results` using the official jupyter notebook.
+
+## More Benchmarks
+
+Below are awesome benchmarks for multimodal understanding from the research community, that are not initially included in the LLaVA-1.5 release.
+
+### Q-Bench
+
+1. Download [`llvisionqa_dev.json`](https://huggingface.co/datasets/nanyangtu/LLVisionQA-QBench/resolve/main/llvisionqa_dev.json) (for `dev`-subset) and [`llvisionqa_test.json`](https://huggingface.co/datasets/nanyangtu/LLVisionQA-QBench/resolve/main/llvisionqa_test.json) (for `test`-subset). Put them under `./playground/data/eval/qbench`. 
+2. Download and extract [images](https://huggingface.co/datasets/nanyangtu/LLVisionQA-QBench/resolve/main/images_llvisionqa.tar) and put all the images directly under `./playground/data/eval/qbench/images_llviqionqa`.
+3. Single-GPU inference (change `dev` to `test` for evaluation on test set).
+```Shell
+CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/qbench.sh dev
+```
+4. Submit the results by instruction [here](https://github.com/VQAssessment/Q-Bench#option-1-submit-results): `./playground/data/eval/qbench/llvisionqa_dev_answers.jsonl`.
+
+### Chinese-Q-Bench
+
+1. Download [`质衡-问答-验证集.json`](https://huggingface.co/datasets/nanyangtu/LLVisionQA-QBench/resolve/main/%E8%B4%A8%E8%A1%A1-%E9%97%AE%E7%AD%94-%E9%AA%8C%E8%AF%81%E9%9B%86.json) (for `dev`-subset) and [`质衡-问答-测试集.json`](https://huggingface.co/datasets/nanyangtu/LLVisionQA-QBench/resolve/main/%E8%B4%A8%E8%A1%A1-%E9%97%AE%E7%AD%94-%E6%B5%8B%E8%AF%95%E9%9B%86.json) (for `test`-subset). Put them under `./playground/data/eval/qbench`. 
+2. Download and extract [images](https://huggingface.co/datasets/nanyangtu/LLVisionQA-QBench/resolve/main/images_llvisionqa.tar) and put all the images directly under `./playground/data/eval/qbench/images_llviqionqa`.
+3. Single-GPU inference (change `dev` to `test` for evaluation on test set).
+```Shell
+CUDA_VISIBLE_DEVICES=0 bash scripts/v1_5/eval/qbench_zh.sh dev
+```
+4. Submit the results by instruction [here](https://github.com/VQAssessment/Q-Bench#option-1-submit-results): `./playground/data/eval/qbench/llvisionqa_zh_dev_answers.jsonl`.

docs/Finetune_Custom_Data.md

@@ -0,0 +1,37 @@
+# Finetune LLaVA on Custom Datasets
+
+## Dataset Format
+
+Convert your data to a JSON file of a List of all samples. Sample metadata should contain `id` (a unique identifier), `image` (the path to the image), and `conversations` (the conversation data between human and AI).
+
+A sample JSON for finetuning LLaVA for generating tag-style captions for Stable Diffusion:
+
+```json
+[
+  {
+    "id": "997bb945-628d-4724-b370-b84de974a19f",
+    "image": "part-000001/997bb945-628d-4724-b370-b84de974a19f.jpg",
+    "conversations": [
+      {
+        "from": "human",
+        "value": "<image>\nWrite a prompt for Stable Diffusion to generate this image."
+      },
+      {
+        "from": "gpt",
+        "value": "a beautiful painting of chernobyl by nekro, pascal blanche, john harris, greg rutkowski, sin jong hun, moebius, simon stalenhag. in style of cg art. ray tracing. cel shading. hyper detailed. realistic. ue 5. maya. octane render. "
+      },
+    ]
+  },
+  ...
+]
+```
+
+## Command
+
+If you have a limited task-specific data, we recommend finetuning from LLaVA checkpoints with LoRA following this [script](https://github.com/haotian-liu/LLaVA/blob/main/scripts/v1_5/finetune_task_lora.sh).
+
+If the amount of the task-specific data is sufficient, you can also finetune from LLaVA checkpoints with full-model finetuning following this [script](https://github.com/haotian-liu/LLaVA/blob/main/scripts/v1_5/finetune_task.sh).
+
+You may need to adjust the hyperparameters to fit each specific dataset and your hardware constraint.
+
+

docs/Intel.md

@@ -0,0 +1,7 @@
+# Intel Platforms 
+
+* Support [Intel GPU Max Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/max-series.html)    
+* Support [Intel CPU Sapphire Rapides](https://ark.intel.com/content/www/us/en/ark/products/codename/126212/products-formerly-sapphire-rapids.html)    
+* Based on [Intel Extension for Pytorch](https://intel.github.io/intel-extension-for-pytorch)    
+
+More details in  [**intel branch**](https://github.com/haotian-liu/LLaVA/tree/intel/docs/intel)

docs/LLaVA_Bench.md

@@ -0,0 +1,31 @@
+# LLaVA-Bench [[Download](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild)]
+
+**-Introduction-**  Large commercial multimodal chatbots have been released in this week, including 
+- [Multimodal Bing-Chat by Microsoft](https://blogs.bing.com/search/july-2023/Bing-Chat-Enterprise-announced,-multimodal-Visual-Search-rolling-out-to-Bing-Chat) (July 18, 2023) 
+- [Multimodal Bard by Google](https://bard.google.com/). 
+
+These chatbots are presumably supported by proprietary large multimodal models (LMM). Compared with the open-source LMM such as LLaVA, proprietary LMM represent the scaling success upperbound of the current SoTA techniques. They share the goal of developing multimodal chatbots that follow human intents to complete various daily-life visual tasks in the wild. While it remains less explored how to evaluate multimodal chat ability, it provides useful feedback to study open-source LMMs against the commercial multimodal chatbots. In addition to the *LLaVA-Bench (COCO)* dataset we used to develop the early versions of LLaVA, we are releasing  [*LLaVA-Bench (In-the-Wild)*](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild) to the community for the public use.
+
+## LLaVA-Bench (In-the-Wild *[Ongoing work]*)
+
+To evaluate the model's capability in more challenging tasks and generalizability to novel domains, we collect a diverse set of 24 images with 60 questions in total, including indoor and outdoor scenes, memes, paintings, sketches, etc, and associate each image with a highly-detailed and manually-curated description and a proper selection of questions. Such design also assesses the model's robustness to different prompts. In this release, we also categorize questions into three categories: conversation (simple QA), detailed description, and complex reasoning. We continue to expand and improve the diversity of the LLaVA-Bench (In-the-Wild).  We manually query Bing-Chat and Bard to get the responses. 
+
+### Results
+
+The score is measured by comparing against a reference answer generated by text-only GPT-4. It is generated by feeding the question, along with the ground truth image annotations as the context. A text-only GPT-4 evaluator rates both answers. We query GPT-4 by putting the reference answer first, and then the answer generated by the candidate model. We upload images at their original resolution to Bard and Bing-Chat to obtain the results.
+
+| Approach       | Conversation | Detail | Reasoning | Overall |
+|----------------|--------------|--------|-----------|---------|
+| Bard-0718      | 83.7         | 69.7   | 78.7      | 77.8    |
+| Bing-Chat-0629 | 59.6         | 52.2   | 90.1      | 71.5    |
+| LLaVA-13B-v1-336px-0719 (beam=1) | 64.3         | 55.9   | 81.7      | 70.1    |
+| LLaVA-13B-v1-336px-0719 (beam=5) | 68.4         | 59.9   | 84.3      | 73.5    |
+
+Note that Bard sometimes refuses to answer questions about images containing humans, and Bing-Chat blurs the human faces in the images. We also provide the benchmark score for the subset without humans.
+
+| Approach       | Conversation | Detail | Reasoning | Overall |
+|----------------|--------------|--------|-----------|---------|
+| Bard-0718      | 94.9         | 74.3   | 84.3      | 84.6    |
+| Bing-Chat-0629 | 55.8         | 53.6   | 93.5      | 72.6    |
+| LLaVA-13B-v1-336px-0719 (beam=1) | 62.2         | 56.4   | 82.2      | 70.0    |
+| LLaVA-13B-v1-336px-0719 (beam=5) | 65.6         | 61.7   | 85.0      | 73.6    |

docs/LLaVA_from_LLaMA2.md

@@ -0,0 +1,29 @@
+# LLaVA (based on Llama 2 LLM, Preview)
+
+*NOTE: This is a technical preview. We are still running hyperparameter search, and will release the final model soon.  If you'd like to contribute to this, please contact us.*
+
+:llama: **-Introduction-** [Llama 2 is an open-source LLM released by Meta AI](https://about.fb.com/news/2023/07/llama-2/) today (July 18, 2023). Compared with its early version [Llama 1](https://ai.meta.com/blog/large-language-model-llama-meta-ai/), Llama 2 is more favored in ***stronger language performance***, ***longer context window***, and importantly ***commercially usable***! While Llama 2 is changing the LLM market landscape in the language space, its multimodal ability remains unknown. We quickly develop the LLaVA variant based on the latest Llama 2 checkpoints, and release it to the community for the public use.
+
+You need to apply for and download the latest Llama 2 checkpoints to start your own training (apply [here](https://ai.meta.com/resources/models-and-libraries/llama-downloads/))
+
+
+## Training
+
+Please checkout [`pretrain.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/pretrain.sh), [`finetune.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune.sh), [`finetune_lora.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune_lora.sh).
+
+## LLaVA (based on Llama 2), What is different? 
+
+:volcano: How is the new LLaVA based on Llama 2 different from Llama 1? The comparisons of the training process are described:
+- **Pre-training**. The pre-trained base LLM is changed from Llama 1 to Llama 2
+- **Language instruction-tuning**. The previous LLaVA model starts with Vicuna, which is instruct tuned on ShareGPT data from Llama 1; The new LLaVA model starts with Llama 2 Chat, which is an instruct tuned checkpoint on dialogue data from Llama 2.
+- **Multimodal instruction-tuning**. The same LLaVA-Lighting process is applied.
+
+
+### Results
+
+- Llama 2 is better at following the instructions of role playing; Llama 2 fails in following the instructions of translation
+- The quantitative evaluation on [LLaVA-Bench](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_Bench.md) demonstrates on-par performance between Llama 2 and Llama 1 in LLaVA's multimodal chat ability.
+
+
+<img src="../images/llava_example_cmp.png" width="100%">
+

docs/LoRA.md

@@ -0,0 +1,46 @@
+# LLaVA (LoRA, Preview)
+
+NOTE: This is a technical preview, and is not yet ready for production use. We are still running hyperparameter search for the LoRA model, and will release the final model soon.  If you'd like to contribute to this, please contact us.
+
+You need latest code base for LoRA support (instructions [here](https://github.com/haotian-liu/LLaVA#upgrade-to-latest-code-base))
+
+## Demo (Web UI)
+
+Please execute each of the commands below one by one (after the previous one has finished).  The commands are the same as launching other demos except for an additional `--model-base` flag to specify the base model to use. Please make sure the base model corresponds to the LoRA checkpoint that you are using.  For this technical preview, you need Vicuna v1.1 (7B) checkpoint (if you do not have that already, follow the instructions [here](https://github.com/lm-sys/FastChat#vicuna-weights)).
+
+#### Launch a controller
+```Shell
+python -m llava.serve.controller --host 0.0.0.0 --port 10000
+```
+
+#### Launch a gradio web server.
+```Shell
+python -m llava.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload
+```
+You just launched the Gradio web interface. Now, you can open the web interface with the URL printed on the screen. You may notice that there is no model in the model list. Do not worry, as we have not launched any model worker yet. It will be automatically updated when you launch a model worker.
+
+#### Launch a model worker
+```Shell
+python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-vicuna-7b-v1.1-lcs_558k-instruct_80k_3e-lora-preview-alpha --model-base /path/to/vicuna-v1.1
+```
+Wait until the process finishes loading the model and you see "Uvicorn running on ...".  Now, refresh your Gradio web UI, and you will see the model you just launched in the model list.
+
+You can launch as many workers as you want, and compare between different model checkpoints in the same Gradio interface. Please keep the `--controller` the same, and modify the `--port` and `--worker` to a different port number for each worker.
+
+
+## Training
+
+Please see sample training scripts for [LoRA](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune_lora.sh) and [QLoRA](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune_qlora.sh).
+
+We provide sample DeepSpeed configs, [`zero3.json`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/zero3.json) is more like PyTorch FSDP, and [`zero3_offload.json`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/zero3_offload.json) can further save memory consumption by offloading parameters to CPU. `zero3.json` is usually faster than `zero3_offload.json` but requires more GPU memory, therefore, we recommend trying `zero3.json` first, and if you run out of GPU memory, try `zero3_offload.json`. You can also tweak the `per_device_train_batch_size` and `gradient_accumulation_steps` in the config to save memory, and just to make sure that `per_device_train_batch_size` and `gradient_accumulation_steps` remains the same.
+
+If you are having issues with ZeRO-3 configs, and there are enough VRAM, you may try [`zero2.json`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/zero2.json). This consumes slightly more memory than ZeRO-3, and behaves more similar to PyTorch FSDP, while still supporting parameter-efficient tuning.
+
+## Create Merged Checkpoints
+
+```Shell
+python scripts/merge_lora_weights.py \
+    --model-path /path/to/lora_model \
+    --model-base /path/to/base_model \
+    --save-model-path /path/to/merge_model
+```

docs/MODEL_ZOO.md

@@ -0,0 +1,150 @@
+# Model Zoo
+
+**To Use LLaVA-1.6 checkpoints, your llava package version must be newer than 1.2.0. [Instructions](https://github.com/haotian-liu/LLaVA#upgrade-to-latest-code-base) on how to upgrade.**
+
+If you are interested in including any other details in Model Zoo, please open an issue :)
+
+The model weights below are *merged* weights. You do not need to apply delta. The usage of LLaVA checkpoints should comply with the base LLM's model license.
+
+## LLaVA-v1.6
+
+| Version | LLM | Schedule | Checkpoint | MMMU | MathVista | VQAv2 | GQA | VizWiz | SQA | TextVQA | POPE | MME | MM-Bench | MM-Bench-CN | SEED-IMG | LLaVA-Bench-Wild | MM-Vet |
+|----------|----------|-----------|-----------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
+| LLaVA-1.6 | Vicuna-7B | full_ft-1e | [liuhaotian/llava-v1.6-vicuna-7b](https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b) | 35.8 | 34.6 | 81.8 | 64.2 | 57.6 | 70.1 | 64.9 | 86.5 | 1519/332 | 67.4 | 60.6 | 70.2 | 81.6 | 43.9 |
+| LLaVA-1.6 | Vicuna-13B | full_ft-1e | [liuhaotian/llava-v1.6-vicuna-13b](https://huggingface.co/liuhaotian/llava-v1.6-vicuna-13b) | 36.2 | 35.3 | 82.8 | 65.4 | 60.5 | 73.6 | 67.1 | 86.2 | 1575/326 | 70 | 64.4 | 71.9 | 87.3 | 48.4 |
+| LLaVA-1.6 | Mistral-7B | full_ft-1e | [liuhaotian/llava-v1.6-mistral-7b](https://huggingface.co/liuhaotian/llava-v1.6-mistral-7b) | 35.3 | 37.7 | 82.2 | 64.8 | 60.0 | 72.8 | 65.7 | 86.7 | 1498/321 | 68.7 | 61.2 | 72.2 | 83.2 | 47.3 |
+| LLaVA-1.6 | Hermes-Yi-34B | full_ft-1e | [liuhaotian/llava-v1.6-34b](https://huggingface.co/liuhaotian/llava-v1.6-34b) | 51.1 | 46.5 | 83.7 | 67.1 | 63.8 | 81.8 | 69.5 | 87.7 | 1631/397 | 79.3 | 79 | 75.9 | 89.6 | 57.4 |
+
+*LLaVA-1.6-34B outperforms Gemini Pro on benchmarks like MMMU and MathVista.*
+
+
+## LLaVA-v1.5
+
+| Version | Size | Schedule | Checkpoint | VQAv2 | GQA | VizWiz | SQA | TextVQA | POPE | MME | MM-Bench | MM-Bench-CN | SEED | LLaVA-Bench-Wild | MM-Vet |
+|----------|----------|-----------|-----------|---|---|---|---|---|---|---|---|---|---|---|---|
+| LLaVA-1.5 | 7B | full_ft-1e | [liuhaotian/llava-v1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b) | 78.5 | 62.0 | 50.0 | 66.8 | 58.2 | 85.9 | 1510.7 | 64.3 | 58.3 | 58.6 | 65.4 | 31.1 |
+| LLaVA-1.5 | 13B | full_ft-1e | [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) | 80.0 | 63.3 | 53.6 | 71.6 | 61.3 | 85.9 | 1531.3 | 67.7 | 63.6 | 61.6 | 72.5 | 36.1 |
+| LLaVA-1.5 | 7B | lora-1e | [liuhaotian/llava-v1.5-7b-lora](https://huggingface.co/liuhaotian/llava-v1.5-7b-lora) | 79.1 | 63.0 | 47.8 | 68.4 | 58.2 | 86.4 | 1476.9 | 66.1 | 58.9 | 60.1 | 67.9 | 30.2 |
+| LLaVA-1.5 | 13B | lora-1e | [liuhaotian/llava-v1.5-13b-lora](https://huggingface.co/liuhaotian/llava-v1.5-13b-lora) | 80.0 | 63.3 | 58.9 | 71.2 | 60.2 | 86.7 | 1541.7 | 68.5 | 61.5 | 61.3 | 69.5 | 38.3 |
+
+Base model: Vicuna v1.5. Training logs: [wandb](https://api.wandb.ai/links/lht/6orh56wc).
+
+<p align="center">
+  <img src="../images/llava_v1_5_radar.jpg" width="500px"> <br>
+  LLaVA-1.5 achieves SoTA performance across 11 benchmarks.
+</p>
+
+
+## LLaVA-v1
+
+*Note: We recommend using the most capable LLaVA-v1.5 series above for the best performance.*
+
+| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | LLaVA-Bench-Conv | LLaVA-Bench-Detail | LLaVA-Bench-Complex | LLaVA-Bench-Overall | Download |
+|----------|----------------|---------------|----------------------|-----------------|--------------------|------------------|--------------------|---------------------|---------------------|---------------------|
+| Vicuna-13B-v1.3 | CLIP-L-336px | LCS-558K | 1e | LLaVA-Instruct-80K | proj-1e, lora-1e | 64.3 | 55.9 | 81.7 | 70.1 | [LoRA](https://huggingface.co/liuhaotian/llava-v1-0719-336px-lora-vicuna-13b-v1.3) [LoRA-Merged](https://huggingface.co/liuhaotian/llava-v1-0719-336px-lora-merge-vicuna-13b-v1.3) |
+| LLaMA-2-13B-Chat | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | full_ft-1e | 56.7 | 58.6 | 80.0 | 67.9 | [ckpt](https://huggingface.co/liuhaotian/llava-llama-2-13b-chat-lightning-preview) |
+| LLaMA-2-7B-Chat | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | lora-1e | 51.2 | 58.9 | 71.6 | 62.8 | [LoRA](https://huggingface.co/liuhaotian/llava-llama-2-7b-chat-lightning-lora-preview) |
+
+
+## Projector weights
+
+These are projector weights we have pretrained. You can use these projector weights for visual instruction tuning. They are just pretrained on image-text pairs and are NOT instruction-tuned, which means they do NOT follow instructions as well as our official models and can output repetitive, lengthy, and garbled outputs. If you want to have nice conversations with LLaVA, use the checkpoints above (LLaVA v1.5).
+
+NOTE: These projector weights are only compatible with `llava>=1.0.0`. Please check out the latest codebase if your local code version is below v1.0.0.
+
+NOTE: When you use our pretrained projector for visual instruction tuning, it is very important to use the same base LLM and vision encoder as the one we used for pretraining the projector. Otherwise, the performance will be very poor.
+
+When using these projector weights to instruction-tune your LMM, please make sure that these options are correctly set as follows,
+
+```Shell
+--mm_use_im_start_end False
+--mm_use_im_patch_token False
+```
+
+| Base LLM | Vision Encoder | Projection | Pretrain Data | Pretraining schedule | Download |
+|----------|----------------|---------------|----------------------|----------|----------|
+| Vicuna-13B-v1.5 | CLIP-L-336px | MLP-2x | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-v1.5-mlp2x-336px-pretrain-vicuna-13b-v1.5) |
+| Vicuna-7B-v1.5 | CLIP-L-336px | MLP-2x | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-v1.5-mlp2x-336px-pretrain-vicuna-7b-v1.5) |
+| LLaMA-2-13B-Chat | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-llama-2-13b-chat) |
+| LLaMA-2-7B-Chat | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-llama-2-7b-chat) |
+| LLaMA-2-13B-Chat | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-llama-2-13b-chat) |
+| LLaMA-2-7B-Chat | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-llama-2-7b-chat) |
+| Vicuna-13B-v1.3 | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-vicuna-13b-v1.3) |
+| Vicuna-7B-v1.3 | CLIP-L-336px | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-336px-pretrain-vicuna-7b-v1.3) |
+| Vicuna-13B-v1.3 | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-vicuna-13b-v1.3) |
+| Vicuna-7B-v1.3 | CLIP-L | Linear | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/llava-pretrain-vicuna-7b-v1.3) |
+
+
+## Science QA Checkpoints
+
+| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | Download |
+|----------|----------------|---------------|----------------------|-----------------|--------------------|---------------------|
+| Vicuna-13B-v1.3 | CLIP-L | LCS-558K | 1e | ScienceQA | full_ft-12e | [ckpt](https://huggingface.co/liuhaotian/llava-lcs558k-scienceqa-vicuna-13b-v1.3) |
+
+
+## Legacy Models (merged weights)
+
+The model weights below are *merged* weights. You do not need to apply delta. The usage of LLaVA checkpoints should comply with the base LLM's model license.
+
+| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | Download |
+|----------|----------------|---------------|----------------------|-----------------|--------------------|------------------|
+| MPT-7B-Chat | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | full_ft-1e | [preview](https://huggingface.co/liuhaotian/LLaVA-Lightning-MPT-7B-preview) |
+
+
+## Legacy Models (delta weights)
+
+The model weights below are *delta* weights. The usage of LLaVA checkpoints should comply with the base LLM's model license: [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md).
+
+You can add our delta to the original LLaMA weights to obtain the LLaVA weights.
+
+Instructions:
+
+1. Get the original LLaMA weights in the huggingface format by following the instructions [here](https://huggingface.co/docs/transformers/main/model_doc/llama).
+2. Use the following scripts to get LLaVA weights by applying our delta. It will automatically download delta weights from our Hugging Face account. In the script below, we use the delta weights of [`liuhaotian/LLaVA-7b-delta-v0`](https://huggingface.co/liuhaotian/LLaVA-7b-delta-v0) as an example. It can be adapted for other delta weights by changing the `--delta` argument (and base/target accordingly).
+
+```bash
+python3 -m llava.model.apply_delta \
+    --base /path/to/llama-7b \
+    --target /output/path/to/LLaVA-7B-v0 \
+    --delta liuhaotian/LLaVA-7b-delta-v0
+```
+
+| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | Download |
+|----------|----------------|---------------|----------------------|-----------------|--------------------|------------------|
+| Vicuna-13B-v1.1 | CLIP-L | CC-595K | 1e | LLaVA-Instruct-158K | full_ft-3e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1) |
+| Vicuna-7B-v1.1 | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | full_ft-1e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-Lightning-7B-delta-v1-1) |
+| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | LLaVA-Instruct-158K | full_ft-3e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v0) |
+| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | ScienceQA | full_ft-12e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v0-science_qa) |
+| Vicuna-7B-v0 | CLIP-L | CC-595K | 1e | LLaVA-Instruct-158K | full_ft-3e | [delta-weights](https://huggingface.co/liuhaotian/LLaVA-7b-delta-v0) |
+
+
+
+## Legacy Projector weights
+
+The following projector weights are deprecated, and the support for them may be removed in the future. They do not support zero-shot inference. Please use the projector weights in the [table above](#projector-weights) if possible.
+
+**NOTE**: When you use our pretrained projector for visual instruction tuning, it is very important to **use the same base LLM and vision encoder** as the one we used for pretraining the projector. Otherwise, the performance will be very bad.
+
+When using these projector weights to instruction tune your LMM, please make sure that these options are correctly set as follows,
+
+```Shell
+--mm_use_im_start_end True
+--mm_use_im_patch_token False
+```
+
+| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Download |
+|----------|----------------|---------------|----------------------|----------|
+| Vicuna-7B-v1.1 | CLIP-L | LCS-558K | 1e | [projector](https://huggingface.co/liuhaotian/LLaVA-Pretrained-Projectors/blob/main/LLaVA-7b-pretrain-projector-v1-1-LCS-558K-blip_caption.bin) |
+| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | [projector](https://huggingface.co/liuhaotian/LLaVA-Pretrained-Projectors/blob/main/LLaVA-13b-pretrain-projector-v0-CC3M-595K-original_caption.bin) |
+| Vicuna-7B-v0 | CLIP-L | CC-595K | 1e | [projector](https://huggingface.co/liuhaotian/LLaVA-Pretrained-Projectors/blob/main/LLaVA-7b-pretrain-projector-v0-CC3M-595K-original_caption.bin) |
+
+When using these projector weights to instruction tune your LMM, please make sure that these options are correctly set as follows,
+
+```Shell
+--mm_use_im_start_end False
+--mm_use_im_patch_token False
+```
+
+| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Download |
+|----------|----------------|---------------|----------------------|----------|
+| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | [projector](https://huggingface.co/liuhaotian/LLaVA-Pretrained-Projectors/blob/main/LLaVA-13b-pretrain-projector-v0-CC3M-595K-original_caption-no_im_token.bin) |

docs/ScienceQA.md

@@ -0,0 +1,53 @@
+### ScienceQA
+
+#### Prepare Data
+1. Please see ScienceQA [repo](https://github.com/lupantech/ScienceQA) for setting up the dataset.
+2. Generate ScienceQA dataset for LLaVA conversation-style format.
+
+```Shell
+python scripts/convert_sqa_to_llava.py \
+    convert_to_llava \
+    --base-dir /path/to/ScienceQA/data/scienceqa \
+    --prompt-format "QCM-LEA" \
+    --split {train,val,minival,test,minitest}
+```
+
+#### Training
+
+1. Pretraining
+
+You can download our pretrained projector weights from our [Model Zoo](), or train your own projector weights using [`pretrain.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/pretrain.sh).
+
+2. Finetuning
+
+See [`finetune_sqa.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/finetune_sqa.sh).
+
+#### Evaluation
+
+1. Multiple-GPU inference
+You may evaluate this with multiple GPUs, and concatenate the generated jsonl files.  Please refer to our script for [batch evaluation](https://github.com/haotian-liu/LLaVA/blob/main/scripts/sqa_eval_batch.sh) and [results gathering](https://github.com/haotian-liu/LLaVA/blob/main/scripts/sqa_eval_gather.sh).
+
+2. Single-GPU inference
+
+(a) Generate LLaVA responses on ScienceQA dataset
+
+```Shell
+python -m llava.eval.model_vqa_science \
+    --model-path liuhaotian/llava-lcs558k-scienceqa-vicuna-13b-v1.3 \
+    --question-file /path/to/ScienceQA/data/scienceqa/llava_test_QCM-LEA.json \
+    --image-folder /path/to/ScienceQA/data/scienceqa/images/test \
+    --answers-file vqa/results/ScienceQA/test_llava-13b.jsonl \
+    --conv-mode llava_v1
+```
+
+(b) Evaluate the generated responses
+
+```Shell
+python eval_science_qa.py \
+    --base-dir /path/to/ScienceQA/data/scienceqa \
+    --result-file vqa/results/ScienceQA/test_llava-13b.jsonl \
+    --output-file vqa/results/ScienceQA/test_llava-13b_output.json \
+    --output-result vqa/results/ScienceQA/test_llava-13b_result.json \
+```
+
+For reference, we attach our prediction file [`test_sqa_llava_lcs_558k_sqa_12e_vicuna_v1_3_13b.json`](https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/table/results/test_sqa_llava_lcs_558k_sqa_12e_vicuna_v1_3_13b.json) and [`test_sqa_llava_13b_v0.json`](https://github.com/haotian-liu/LLaVA/blob/main/llava/eval/table/results/test_sqa_llava_13b_v0.json) for comparison when reproducing our results, as well as for further analysis in detail.

docs/Windows.md

@@ -0,0 +1,27 @@
+# Run LLaVA on Windows
+
+*NOTE: LLaVA on Windows is not fully supported. Currently we only support 16-bit inference. For a more complete support, please use [WSL2](https://learn.microsoft.com/en-us/windows/wsl/install) for now. More functionalities on Windows is to be added soon, stay tuned.*
+
+## Installation
+
+1. Clone this repository and navigate to LLaVA folder
+```bash
+git clone https://github.com/haotian-liu/LLaVA.git
+cd LLaVA
+```
+
+2. Install Package
+```Shell
+conda create -n llava python=3.10 -y
+conda activate llava
+python -mpip install --upgrade pip  # enable PEP 660 support
+pip install torch==2.0.1+cu117 torchvision==0.15.2+cu117 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu117
+pip install -e .
+pip uninstall bitsandbytes
+```
+
+## Run demo
+
+See instructions [here](https://github.com/haotian-liu/LLaVA#demo).
+
+Note that quantization (4-bit, 8-bit) is *NOT* supported on Windows. Stay tuned for the 4-bit support on Windows!

docs/macOS.md

@@ -0,0 +1,29 @@
+# Run LLaVA on macOS
+
+*NOTE: LLaVA on macOS is not fully supported. Currently we only support 16-bit inference. More functionalities on macOS is to be added soon, stay tuned.*
+
+## Installation
+
+1. Clone this repository and navigate to LLaVA folder
+```bash
+git clone https://github.com/haotian-liu/LLaVA.git
+cd LLaVA
+```
+
+2. Install Package
+```Shell
+conda create -n llava python=3.10 -y
+conda activate llava
+python -mpip install --upgrade pip  # enable PEP 660 support
+pip install -e .
+pip install torch==2.1.0 torchvision==0.16.0
+pip uninstall bitsandbytes
+```
+
+## Run demo
+
+Specify `--device mps` when launching model worker or CLI.
+
+See instructions [here](https://github.com/haotian-liu/LLaVA#demo).
+
+Note that quantization (4-bit, 8-bit) is *NOT* supported on macOS. Stay tuned for the 4-bit support on macOS!

llava.egg-info/PKG-INFO

@@ -0,0 +1,494 @@
+Metadata-Version: 2.1
+Name: llava
+Version: 1.2.2.post1
+Summary: Towards GPT-4 like large language and visual assistant.
+Project-URL: Homepage, https://llava-vl.github.io
+Project-URL: Bug Tracker, https://github.com/haotian-liu/LLaVA/issues
+Classifier: Programming Language :: Python :: 3
+Classifier: License :: OSI Approved :: Apache Software License
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: torch==2.1.2
+Requires-Dist: torchvision==0.16.2
+Requires-Dist: transformers==4.37.2
+Requires-Dist: tokenizers==0.15.1
+Requires-Dist: sentencepiece==0.1.99
+Requires-Dist: shortuuid
+Requires-Dist: accelerate==0.21.0
+Requires-Dist: peft
+Requires-Dist: bitsandbytes
+Requires-Dist: pydantic
+Requires-Dist: markdown2[all]
+Requires-Dist: numpy
+Requires-Dist: scikit-learn==1.2.2
+Requires-Dist: gradio==4.16.0
+Requires-Dist: gradio_client==0.8.1
+Requires-Dist: requests
+Requires-Dist: httpx==0.24.0
+Requires-Dist: uvicorn
+Requires-Dist: fastapi
+Requires-Dist: einops==0.6.1
+Requires-Dist: einops-exts==0.0.4
+Requires-Dist: timm==0.6.13
+Provides-Extra: train
+Requires-Dist: deepspeed==0.12.6; extra == "train"
+Requires-Dist: ninja; extra == "train"
+Requires-Dist: wandb; extra == "train"
+Provides-Extra: build
+Requires-Dist: build; extra == "build"
+Requires-Dist: twine; extra == "build"
+
+# 🌋 LLaVA: Large Language and Vision Assistant
+
+*Visual instruction tuning towards large language and vision models with GPT-4 level capabilities.*
+
+[📢 [LLaVA-NeXT Blog](https://llava-vl.github.io/blog/2024-01-30-llava-next/)] [[Project Page](https://llava-vl.github.io/)] [[Demo](https://llava.hliu.cc/)]  [[Data](https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md)] [[Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)]
+
+🤝Community Contributions: [[llama.cpp](https://github.com/ggerganov/llama.cpp/pull/3436)] [[Colab](https://github.com/camenduru/LLaVA-colab)] [[🤗Space](https://huggingface.co/spaces/badayvedat/LLaVA)] [[Replicate](https://replicate.com/yorickvp/llava-13b)] [[AutoGen](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_lmm_llava.ipynb)]  [[BakLLaVA](https://github.com/SkunkworksAI/BakLLaVA)]
+
+**Improved Baselines with Visual Instruction Tuning** [[Paper](https://arxiv.org/abs/2310.03744)] [[HF](https://huggingface.co/papers/2310.03744)] <br>
+[Haotian Liu](https://hliu.cc), [Chunyuan Li](https://chunyuan.li/), [Yuheng Li](https://yuheng-li.github.io/), [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/)
+
+**Visual Instruction Tuning** (NeurIPS 2023, **Oral**) [[Paper](https://arxiv.org/abs/2304.08485)] [[HF](https://huggingface.co/papers/2304.08485)] <br>
+[Haotian Liu*](https://hliu.cc), [Chunyuan Li*](https://chunyuan.li/), [Qingyang Wu](https://scholar.google.ca/citations?user=HDiw-TsAAAAJ&hl=en/), [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/) (*Equal Contribution)
+
+<!--p align="center">
+    <a href="https://llava.hliu.cc/"><img src="images/llava_logo.png" width="50%"></a> <br>
+    Generated by <a href="https://gligen.github.io/">GLIGEN</a> via "a cute lava llama with glasses" and box prompt
+</p-->
+
+
+## Release
+- [1/30] 🔥 LLaVA-NeXT is out! With additional scaling to LLaVA-1.5, LLaVA-NeXT-34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before. Check out the [blog post](https://llava-vl.github.io/blog/2024-01-30-llava-next/), and explore the [demo](https://llava.hliu.cc/)! Models are available in [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md). Training/eval data and scripts coming soon.
+- [11/10] [LLaVA-Plus](https://llava-vl.github.io/llava-plus/) is released: Learning to Use Tools for Creating Multimodal Agents, with LLaVA-Plus (LLaVA that Plug and Learn to Use Skills). [[Project Page](https://llava-vl.github.io/llava-plus/)] [[Demo](https://llavaplus.ngrok.io/)] [[Code](https://github.com/LLaVA-VL/LLaVA-Plus-Codebase)] [[Paper](https://arxiv.org/abs/2311.05437)]
+- [11/2] [LLaVA-Interactive](https://llava-vl.github.io/llava-interactive/) is released: Experience the future of human-AI multimodal interaction with an all-in-one demo for Image Chat, Segmentation, Generation and Editing. [[Project Page](https://llava-vl.github.io/llava-interactive/)] [[Demo](https://llavainteractive.ngrok.io/)] [[Code](https://github.com/LLaVA-VL/LLaVA-Interactive-Demo)] [[Paper](https://arxiv.org/abs/2311.00571)]
+- [10/26] 🔥 LLaVA-1.5 with LoRA achieves comparable performance as full-model finetuning, with a reduced GPU RAM requirement ([ckpts](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md#llava-v15), [script](https://github.com/haotian-liu/LLaVA#train)). We also provide a [doc](https://github.com/haotian-liu/LLaVA/blob/main/docs/Finetune_Custom_Data.md) on how to finetune LLaVA-1.5 on your own dataset with LoRA.
+- [10/12] Check out the Korean LLaVA (Ko-LLaVA), created by ETRI, who has generously supported our research! [[🤗 Demo](https://huggingface.co/spaces/etri-vilab/Ko-LLaVA)]
+- [10/5] 🔥 LLaVA-1.5 is out! Achieving SoTA on 11 benchmarks, with just simple modifications to the original LLaVA, utilizes all public data, completes training in ~1 day on a single 8-A100 node, and surpasses methods like Qwen-VL-Chat that use billion-scale data. Check out the [technical report](https://arxiv.org/abs/2310.03744), and explore the [demo](https://llava.hliu.cc/)! Models are available in [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md). The training data and scripts of LLaVA-1.5 are released [here](https://github.com/haotian-liu/LLaVA#train), and evaluation scripts are released [here](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md)!
+- [9/26] LLaVA is improved with reinforcement learning from human feedback (RLHF) to improve fact grounding and reduce hallucination. Check out the new SFT and RLHF checkpoints at project [[LLavA-RLHF]](https://llava-rlhf.github.io/)
+- [9/22] [LLaVA](https://arxiv.org/abs/2304.08485) is accepted by NeurIPS 2023 as **oral presentation**, and [LLaVA-Med](https://arxiv.org/abs/2306.00890) is accepted by NeurIPS 2023 Datasets and Benchmarks Track as **spotlight presentation**.
+
+<details>
+<summary>More</summary>
+
+- [11/6] Support **Intel** dGPU and CPU platforms. [More details here.](https://github.com/haotian-liu/LLaVA/tree/intel/docs/intel)
+- [10/12] LLaVA is now supported in [llama.cpp](https://github.com/ggerganov/llama.cpp/pull/3436) with 4-bit / 5-bit quantization support!
+- [10/11] The training data and scripts of LLaVA-1.5 are released [here](https://github.com/haotian-liu/LLaVA#train), and evaluation scripts are released [here](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md)!
+- [10/10] [Roboflow Deep Dive](https://blog.roboflow.com/first-impressions-with-llava-1-5/): First Impressions with LLaVA-1.5.
+- [9/20] We summarize our empirical study of training 33B and 65B LLaVA models in a [note](https://arxiv.org/abs/2309.09958). Further, if you are interested in the comprehensive review, evolution and trend of multimodal foundation models, please check out our recent survey paper [``Multimodal Foundation Models: From Specialists to General-Purpose Assistants''.](https://arxiv.org/abs/2309.10020)
+<p align="center">
+  <img src="https://github.com/Computer-Vision-in-the-Wild/CVinW_Readings/blob/main/images/mfm_evolution.jpeg?raw=true" width=50%/>
+</p>
+
+- [7/19] 🔥 We release a major upgrade, including support for LLaMA-2, LoRA training, 4-/8-bit inference, higher resolution (336x336), and a lot more. We release [LLaVA Bench](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_Bench.md) for benchmarking open-ended visual chat with results from Bard and Bing-Chat. We also support and verify training with RTX 3090 and RTX A6000. Check out [LLaVA-from-LLaMA-2](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_from_LLaMA2.md), and our [model zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)!
+- [6/26] [CVPR 2023 Tutorial](https://vlp-tutorial.github.io/) on **Large Multimodal Models: Towards Building and Surpassing Multimodal GPT-4**!  Please check out [[Slides](https://datarelease.blob.core.windows.net/tutorial/vision_foundation_models_2023/slides/Chunyuan_cvpr2023_tutorial_lmm.pdf)] [[Notes](https://arxiv.org/abs/2306.14895)] [[YouTube](https://youtu.be/mkI7EPD1vp8)] [[Bilibli](https://www.bilibili.com/video/BV1Ng4y1T7v3/)].
+- [6/11] We released the preview for the most requested feature: DeepSpeed and LoRA support!  Please see documentations [here](./docs/LoRA.md).
+- [6/1] We released **LLaVA-Med: Large Language and Vision Assistant for Biomedicine**, a step towards building biomedical domain large language and vision models with GPT-4 level capabilities.  Checkout the [paper](https://arxiv.org/abs/2306.00890) and [page](https://github.com/microsoft/LLaVA-Med).
+- [5/6] We are releasing [LLaVA-Lighting-MPT-7B-preview](https://huggingface.co/liuhaotian/LLaVA-Lightning-MPT-7B-preview), based on MPT-7B-Chat!  See [here](#LLaVA-MPT-7b) for more details.
+- [5/2] 🔥 We are releasing LLaVA-Lighting!  Train a lite, multimodal GPT-4 with just $40 in 3 hours!  See [here](#train-llava-lightning) for more details.
+- [4/27] Thanks to the community effort, LLaVA-13B with 4-bit quantization allows you to run on a GPU with as few as 12GB VRAM!  Try it out [here](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/llava).
+- [4/17] 🔥 We released **LLaVA: Large Language and Vision Assistant**. We propose visual instruction tuning, towards building large language and vision models with GPT-4 level capabilities.  Checkout the [paper](https://arxiv.org/abs/2304.08485) and [demo](https://llava.hliu.cc/).
+
+</details>
+
+<!-- <a href="https://llava.hliu.cc/"><img src="assets/demo.gif" width="70%"></a> -->
+
+[![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE)
+**Usage and License Notices**: This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses, including but not limited to the [OpenAI Terms of Use](https://openai.com/policies/terms-of-use) for the dataset and the specific licenses for base language models for checkpoints trained using the dataset (e.g. [Llama community license](https://ai.meta.com/llama/license/) for LLaMA-2 and Vicuna-v1.5). This project does not impose any additional constraints beyond those stipulated in the original licenses. Furthermore, users are reminded to ensure that their use of the dataset and checkpoints is in compliance with all applicable laws and regulations.
+
+
+## Contents
+- [Install](#install)
+- [LLaVA Weights](#llava-weights)
+- [Demo](#Demo)
+- [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)
+- [Dataset](https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md)
+- [Train](#train)
+- [Evaluation](#evaluation)
+
+## Install
+
+If you are not using Linux, do *NOT* proceed, see instructions for [macOS](https://github.com/haotian-liu/LLaVA/blob/main/docs/macOS.md) and [Windows](https://github.com/haotian-liu/LLaVA/blob/main/docs/Windows.md).
+
+1. Clone this repository and navigate to LLaVA folder
+```bash
+git clone https://github.com/haotian-liu/LLaVA.git
+cd LLaVA
+```
+
+2. Install Package
+```Shell
+conda create -n llava python=3.10 -y
+conda activate llava
+pip install --upgrade pip  # enable PEP 660 support
+pip install -e .
+```
+
+3. Install additional packages for training cases
+```
+pip install -e ".[train]"
+pip install flash-attn --no-build-isolation
+```
+
+### Upgrade to latest code base
+
+```Shell
+git pull
+pip install -e .
+```
+
+### Quick Start With HuggingFace
+
+<details>
+<summary>Example Code</summary>
+
+```Python
+from llava.model.builder import load_pretrained_model
+from llava.mm_utils import get_model_name_from_path
+from llava.eval.run_llava import eval_model
+
+model_path = "liuhaotian/llava-v1.5-7b"
+
+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)
+)
+```
+
+Check out the details wth the `load_pretrained_model` function in `llava/model/builder.py`.
+
+You can also use the `eval_model` function in `llava/eval/run_llava.py` to get the output easily. By doing so, you can use this code on Colab directly after downloading this repository.
+
+``` python
+model_path = "liuhaotian/llava-v1.5-7b"
+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": None,
+    "image_file": image_file,
+    "sep": ",",
+    "temperature": 0,
+    "top_p": None,
+    "num_beams": 1,
+    "max_new_tokens": 512
+})()
+
+eval_model(args)
+```
+</details>
+
+## LLaVA Weights
+Please check out our [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md) for all public LLaVA checkpoints, and the instructions of how to use the weights.
+
+## Demo
+
+### Gradio Web UI
+
+To launch a Gradio demo locally, please run the following commands one by one. If you plan to launch multiple model workers to compare between different checkpoints, you only need to launch the controller and the web server *ONCE*.
+
+```mermaid
+flowchart BT
+    %% Declare Nodes
+    gws("Gradio (UI Server)")
+    c("Controller (API Server):<br/>PORT: 10000")
+    mw7b("Model Worker:<br/>llava-v1.5-7b<br/>PORT: 40000")
+    mw13b("Model Worker:<br/>llava-v1.5-13b<br/>PORT: 40001")
+    sglw13b("SGLang Backend:<br/>llava-v1.6-34b<br/>http://localhost:30000")
+    lsglw13b("SGLang Worker:<br/>llava-v1.6-34b<br/>PORT: 40002")
+
+    %% Declare Styles
+    classDef data fill:#3af,stroke:#48a,stroke-width:2px,color:#444
+    classDef success fill:#8f8,stroke:#0a0,stroke-width:2px,color:#444
+    classDef failure fill:#f88,stroke:#f00,stroke-width:2px,color:#444
+
+    %% Assign Styles
+    class id,od data;
+    class cimg,cs_s,scsim_s success;
+    class ncimg,cs_f,scsim_f failure;
+
+    subgraph Demo Connections
+        direction BT
+        c<-->gws
+        
+        mw7b<-->c
+        mw13b<-->c
+        lsglw13b<-->c
+        sglw13b<-->lsglw13b
+    end
+```
+
+#### Launch a controller
+```Shell
+python -m llava.serve.controller --host 0.0.0.0 --port 10000
+```
+
+#### Launch a gradio web server.
+```Shell
+python -m llava.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload
+```
+You just launched the Gradio web interface. Now, you can open the web interface with the URL printed on the screen. You may notice that there is no model in the model list. Do not worry, as we have not launched any model worker yet. It will be automatically updated when you launch a model worker.
+
+#### Launch a SGLang worker
+
+This is the recommended way to serve LLaVA model with high throughput, and you need to install SGLang first. Note that currently `4-bit` quantization is not supported yet on SGLang-LLaVA, and if you have limited GPU VRAM, please check out model worker with [quantization](https://github.com/haotian-liu/LLaVA?tab=readme-ov-file#launch-a-model-worker-4-bit-8-bit-inference-quantized).
+
+```Shell
+pip install "sglang[all]"
+```
+
+You'll first launch a SGLang backend worker which will execute the models on GPUs. Remember the `--port` you've set and you'll use that later.
+
+```Shell
+# Single GPU
+CUDA_VISIBLE_DEVICES=0 python3 -m sglang.launch_server --model-path liuhaotian/llava-v1.5-7b --tokenizer-path llava-hf/llava-1.5-7b-hf --port 30000
+
+# Multiple GPUs with tensor parallel
+CUDA_VISIBLE_DEVICES=0,1 python3 -m sglang.launch_server --model-path liuhaotian/llava-v1.5-13b --tokenizer-path llava-hf/llava-1.5-13b-hf --port 30000 --tp 2
+```
+
+You'll then launch a LLaVA-SGLang worker that will communicate between LLaVA controller and SGLang backend to route the requests. Set `--sgl-endpoint` to `http://127.0.0.1:port` where `port` is the one you just set (default: 30000).
+
+```Shell
+python -m llava.serve.sglang_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --sgl-endpoint http://127.0.0.1:30000
+```
+
+#### Launch a model worker
+
+This is the actual *worker* that performs the inference on the GPU.  Each worker is responsible for a single model specified in `--model-path`.
+
+```Shell
+python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1.5-13b
+```
+Wait until the process finishes loading the model and you see "Uvicorn running on ...".  Now, refresh your Gradio web UI, and you will see the model you just launched in the model list.
+
+You can launch as many workers as you want, and compare between different model checkpoints in the same Gradio interface. Please keep the `--controller` the same, and modify the `--port` and `--worker` to a different port number for each worker.
+```Shell
+python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port <different from 40000, say 40001> --worker http://localhost:<change accordingly, i.e. 40001> --model-path <ckpt2>
+```
+
+If you are using an Apple device with an M1 or M2 chip, you can specify the mps device by using the `--device` flag: `--device mps`.
+
+#### Launch a model worker (Multiple GPUs, when GPU VRAM <= 24GB)
+
+If the VRAM of your GPU is less than 24GB (e.g., RTX 3090, RTX 4090, etc.), you may try running it with multiple GPUs. Our latest code base will automatically try to use multiple GPUs if you have more than one GPU. You can specify which GPUs to use with `CUDA_VISIBLE_DEVICES`. Below is an example of running with the first two GPUs.
+
+```Shell
+CUDA_VISIBLE_DEVICES=0,1 python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1.5-13b
+```
+
+#### Launch a model worker (4-bit, 8-bit inference, quantized)
+
+You can launch the model worker with quantized bits (4-bit, 8-bit), which allows you to run the inference with reduced GPU memory footprint, potentially allowing you to run on a GPU with as few as 12GB VRAM. Note that inference with quantized bits may not be as accurate as the full-precision model. Simply append `--load-4bit` or `--load-8bit` to the **model worker** command that you are executing. Below is an example of running with 4-bit quantization.
+
+```Shell
+python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1.5-13b --load-4bit
+```
+
+#### Launch a model worker (LoRA weights, unmerged)
+
+You can launch the model worker with LoRA weights, without merging them with the base checkpoint, to save disk space. There will be additional loading time, while the inference speed is the same as the merged checkpoints. Unmerged LoRA checkpoints do not have `lora-merge` in the model name, and are usually much smaller (less than 1GB) than the merged checkpoints (13G for 7B, and 25G for 13B).
+
+To load unmerged LoRA weights, you simply need to pass an additional argument `--model-base`, which is the base LLM that is used to train the LoRA weights. You can check the base LLM of each LoRA weights in the [model zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md).
+
+```Shell
+python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1-0719-336px-lora-vicuna-13b-v1.3 --model-base lmsys/vicuna-13b-v1.3
+```
+
+### CLI Inference
+
+Chat about images using LLaVA without the need of Gradio interface. It also supports multiple GPUs, 4-bit and 8-bit quantized inference. With 4-bit quantization, for our LLaVA-1.5-7B, it uses less than 8GB VRAM on a single GPU.
+
+```Shell
+python -m llava.serve.cli \
+    --model-path liuhaotian/llava-v1.5-7b \
+    --image-file "https://llava-vl.github.io/static/images/view.jpg" \
+    --load-4bit
+```
+
+<img src="images/demo_cli.gif" width="70%">
+
+## Train
+
+*Below is the latest training configuration for LLaVA v1.5. For legacy models, please refer to README of [this](https://github.com/haotian-liu/LLaVA/tree/v1.0.1) version for now. We'll add them in a separate doc later.*
+
+LLaVA training consists of two stages: (1) feature alignment stage: use our 558K subset of the LAION-CC-SBU dataset to connect a *frozen pretrained* vision encoder to a *frozen LLM*; (2) visual instruction tuning stage: use 150K GPT-generated multimodal instruction-following data, plus around 515K VQA data from academic-oriented tasks, to teach the model to follow multimodal instructions.
+
+LLaVA is trained on 8 A100 GPUs with 80GB memory. To train on fewer GPUs, you can reduce the `per_device_train_batch_size` and increase the `gradient_accumulation_steps` accordingly. Always keep the global batch size the same: `per_device_train_batch_size` x `gradient_accumulation_steps` x `num_gpus`.
+
+### Hyperparameters
+We use a similar set of hyperparameters as Vicuna in finetuning.  Both hyperparameters used in pretraining and finetuning are provided below.
+
+1. Pretraining
+
+| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| LLaVA-v1.5-13B | 256 | 1e-3 | 1 | 2048 | 0 |
+
+2. Finetuning
+
+| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| LLaVA-v1.5-13B | 128 | 2e-5 | 1 | 2048 | 0 |
+
+### Download Vicuna checkpoints (automatically)
+
+Our base model Vicuna v1.5, which is an instruction-tuned chatbot, will be downloaded automatically when you run our provided training scripts. No action is needed.
+
+### Pretrain (feature alignment)
+
+Please download the 558K subset of the LAION-CC-SBU dataset with BLIP captions we use in the paper [here](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain).
+
+Pretrain takes around 5.5 hours for LLaVA-v1.5-13B on 8x A100 (80G), due to the increased resolution to 336px. It takes around 3.5 hours for LLaVA-v1.5-7B.
+
+Training script with DeepSpeed ZeRO-2: [`pretrain.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/v1_5/pretrain.sh).
+
+- `--mm_projector_type mlp2x_gelu`: the two-layer MLP vision-language connector.
+- `--vision_tower openai/clip-vit-large-patch14-336`: CLIP ViT-L/14 336px.
+
+<details>
+<summary>Pretrain takes around 20 hours for LLaVA-7B on 8x V100 (32G)</summary>
+
+ We provide training script with DeepSpeed [here](https://github.com/haotian-liu/LLaVA/blob/main/scripts/pretrain_xformers.sh).
+Tips:
+- If you are using V100 which is not supported by FlashAttention, you can use the [memory-efficient attention](https://arxiv.org/abs/2112.05682) implemented in [xFormers](https://github.com/facebookresearch/xformers). Install xformers and replace `llava/train/train_mem.py` above with [llava/train/train_xformers.py](llava/train/train_xformers.py).
+</details>
+
+### Visual Instruction Tuning
+
+1. Prepare data
+
+Please download the annotation of the final mixture our instruction tuning data [llava_v1_5_mix665k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json), and download the images from constituting datasets:
+
+- COCO: [train2017](http://images.cocodataset.org/zips/train2017.zip)
+- GQA: [images](https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip)
+- OCR-VQA: [download script](https://drive.google.com/drive/folders/1_GYPY5UkUy7HIcR0zq3ZCFgeZN7BAfm_?usp=sharing), **we save all files as `.jpg`**
+- TextVQA: [train_val_images](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip)
+- VisualGenome: [part1](https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip), [part2](https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip)
+
+After downloading all of them, organize the data as follows in `./playground/data`,
+
+```
+├── coco
+│   └── train2017
+├── gqa
+│   └── images
+├── ocr_vqa
+│   └── images
+├── textvqa
+│   └── train_images
+└── vg
+    ├── VG_100K
+    └── VG_100K_2
+```
+
+2. Start training!
+
+You may download our pretrained projectors in [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md). It is not recommended to use legacy projectors, as they may be trained with a different version of the codebase, and if any option is off, the model will not function/train as we expected.
+
+Visual instruction tuning takes around 20 hours for LLaVA-v1.5-13B on 8x A100 (80G), due to the increased resolution to 336px. It takes around 10 hours for LLaVA-v1.5-7B on 8x A100 (40G).
+
+Training script with DeepSpeed ZeRO-3: [`finetune.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/v1_5/finetune.sh).
+
+If you are do not have enough GPU memory:
+
+- Use LoRA: [`finetune_lora.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/v1_5/finetune_lora.sh). We are able to fit 13B training in 8-A100-40G/8-A6000, and 7B training in 8-RTX3090. Make sure `per_device_train_batch_size*gradient_accumulation_steps` is the same as the provided script for best reproducibility.
+- Replace `zero3.json` with `zero3_offload.json` which offloads some parameters to CPU RAM. This slows down the training speed.
+
+If you are interested in finetuning LLaVA model to your own task/data, please check out [`Finetune_Custom_Data.md`](https://github.com/haotian-liu/LLaVA/blob/main/docs/Finetune_Custom_Data.md)。
+
+New options to note:
+
+- `--mm_projector_type mlp2x_gelu`: the two-layer MLP vision-language connector.
+- `--vision_tower openai/clip-vit-large-patch14-336`: CLIP ViT-L/14 336px.
+- `--image_aspect_ratio pad`: this pads the non-square images to square, instead of cropping them; it slightly reduces hallucination.
+- `--group_by_modality_length True`: this should only be used when your instruction tuning dataset contains both language (e.g. ShareGPT) and multimodal (e.g. LLaVA-Instruct). It makes the training sampler only sample a single modality (either image or language) during training, which we observe to speed up training by ~25%, and does not affect the final outcome.
+
+## Evaluation
+
+In LLaVA-1.5, we evaluate models on a diverse set of 12 benchmarks. To ensure the reproducibility, we evaluate the models with greedy decoding. We do not evaluate using beam search to make the inference process consistent with the chat demo of real-time outputs.
+
+See [Evaluation.md](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md).
+
+### GPT-assisted Evaluation
+
+Our GPT-assisted evaluation pipeline for multimodal modeling is provided for a comprehensive understanding of the capabilities of vision-language models.  Please see our paper for more details.
+
+1. Generate LLaVA responses
+
+```Shell
+python model_vqa.py \
+    --model-path ./checkpoints/LLaVA-13B-v0 \
+    --question-file \
+    playground/data/coco2014_val_qa_eval/qa90_questions.jsonl \
+    --image-folder \
+    /path/to/coco2014_val \
+    --answers-file \
+    /path/to/answer-file-our.jsonl
+```
+
+2. Evaluate the generated responses.  In our case, [`answer-file-ref.jsonl`](./playground/data/coco2014_val_qa_eval/qa90_gpt4_answer.jsonl) is the response generated by text-only GPT-4 (0314), with the context captions/boxes provided.
+
+```Shell
+OPENAI_API_KEY="sk-***********************************" python llava/eval/eval_gpt_review_visual.py \
+    --question playground/data/coco2014_val_qa_eval/qa90_questions.jsonl \
+    --context llava/eval/table/caps_boxes_coco2014_val_80.jsonl \
+    --answer-list \
+    /path/to/answer-file-ref.jsonl \
+    /path/to/answer-file-our.jsonl \
+    --rule llava/eval/table/rule.json \
+    --output /path/to/review.json
+```
+
+3. Summarize the evaluation results
+
+```Shell
+python summarize_gpt_review.py
+```
+
+## Citation
+
+If you find LLaVA useful for your research and applications, please cite using this BibTeX:
+```bibtex
+@misc{liu2024llavanext,
+    title={LLaVA-NeXT: Improved reasoning, OCR, and world knowledge},
+    url={https://llava-vl.github.io/blog/2024-01-30-llava-next/},
+    author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Li, Bo and Zhang, Yuanhan and Shen, Sheng and Lee, Yong Jae},
+    month={January},
+    year={2024}
+}
+
+@misc{liu2023improvedllava,
+      title={Improved Baselines with Visual Instruction Tuning}, 
+      author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Lee, Yong Jae},
+      publisher={arXiv:2310.03744},
+      year={2023},
+}
+
+@misc{liu2023llava,
+      title={Visual Instruction Tuning}, 
+      author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
+      publisher={NeurIPS},
+      year={2023},
+}
+```
+
+## Acknowledgement
+
+- [Vicuna](https://github.com/lm-sys/FastChat): the codebase we built upon, and our base model Vicuna-13B that has the amazing language capabilities!
+
+## Related Projects
+
+- [Instruction Tuning with GPT-4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://github.com/microsoft/LLaVA-Med)
+- [Otter: In-Context Multi-Modal Instruction Tuning](https://github.com/Luodian/Otter)
+
+For future project ideas, please check out:
+- [SEEM: Segment Everything Everywhere All at Once](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once)
+- [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything) to detect, segment, and generate anything by marrying [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) and [Segment-Anything](https://github.com/facebookresearch/segment-anything).

llava.egg-info/SOURCES.txt

@@ -0,0 +1,58 @@
+LICENSE
+README.md
+pyproject.toml
+llava/__init__.py
+llava/constants.py
+llava/conversation.py
+llava/mm_utils.py
+llava/utils.py
+llava.egg-info/PKG-INFO
+llava.egg-info/SOURCES.txt
+llava.egg-info/dependency_links.txt
+llava.egg-info/requires.txt
+llava.egg-info/top_level.txt
+llava/eval/eval_gpt_review.py
+llava/eval/eval_gpt_review_bench.py
+llava/eval/eval_gpt_review_visual.py
+llava/eval/eval_pope.py
+llava/eval/eval_science_qa.py
+llava/eval/eval_science_qa_gpt4.py
+llava/eval/eval_science_qa_gpt4_requery.py
+llava/eval/eval_textvqa.py
+llava/eval/generate_webpage_data_from_table.py
+llava/eval/m4c_evaluator.py
+llava/eval/model_qa.py
+llava/eval/model_vqa.py
+llava/eval/model_vqa_loader.py
+llava/eval/model_vqa_mmbench.py
+llava/eval/model_vqa_science.py
+llava/eval/qa_baseline_gpt35.py
+llava/eval/run_llava.py
+llava/eval/summarize_gpt_review.py
+llava/model/__init__.py
+llava/model/apply_delta.py
+llava/model/builder.py
+llava/model/consolidate.py
+llava/model/llava_arch.py
+llava/model/make_delta.py
+llava/model/utils.py
+llava/model/language_model/llava_llama.py
+llava/model/language_model/llava_mistral.py
+llava/model/language_model/llava_mpt.py
+llava/model/multimodal_encoder/builder.py
+llava/model/multimodal_encoder/clip_encoder.py
+llava/model/multimodal_projector/builder.py
+llava/serve/__init__.py
+llava/serve/cli.py
+llava/serve/controller.py
+llava/serve/gradio_web_server.py
+llava/serve/model_worker.py
+llava/serve/register_worker.py
+llava/serve/sglang_worker.py
+llava/serve/test_message.py
+llava/train/llama_flash_attn_monkey_patch.py
+llava/train/llama_xformers_attn_monkey_patch.py
+llava/train/llava_trainer.py
+llava/train/train.py
+llava/train/train_mem.py
+llava/train/train_xformers.py

llava.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

llava.egg-info/requires.txt

@@ -0,0 +1,31 @@
+torch==2.1.2
+torchvision==0.16.2
+transformers==4.37.2
+tokenizers==0.15.1
+sentencepiece==0.1.99
+shortuuid
+accelerate==0.21.0
+peft
+bitsandbytes
+pydantic
+markdown2[all]
+numpy
+scikit-learn==1.2.2
+gradio==4.16.0
+gradio_client==0.8.1
+requests
+httpx==0.24.0
+uvicorn
+fastapi
+einops==0.6.1
+einops-exts==0.0.4
+timm==0.6.13
+
+[build]
+build
+twine
+
+[train]
+deepspeed==0.12.6
+ninja
+wandb

llava.egg-info/top_level.txt

@@ -0,0 +1,2 @@
+images
+llava

llava/__init__.py

@@ -0,0 +1 @@
+from .model import LlavaLlamaForCausalLM

llava/constants.py

@@ -0,0 +1,13 @@
+CONTROLLER_HEART_BEAT_EXPIRATION = 30
+WORKER_HEART_BEAT_INTERVAL = 15
+
+LOGDIR = "."
+
+# Model Constants
+IGNORE_INDEX = -100
+IMAGE_TOKEN_INDEX = -200
+DEFAULT_IMAGE_TOKEN = "<image>"
+DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
+DEFAULT_IM_START_TOKEN = "<im_start>"
+DEFAULT_IM_END_TOKEN = "<im_end>"
+IMAGE_PLACEHOLDER = "<image-placeholder>"