Package: llava-torch
🌋 LLaVA: Large Language and Vision AssistantVisual instruction tuning towards large language and vision models with GPT-4 level capabilities.[📢LLaVA-1.6 Blog] [Project Page] [Demo]  [Data] [Model Zoo]🤝Community Contributions: [llama.cpp] [Colab] [🤗Space] [Replicate] [AutoGen]  [BakLLaVA]Improved Baselines with Visual Instruction Tuning[Paper] [HF]Haotian Liu,Chunyuan Li,Yuheng Li,Yong Jae LeeVisual Instruction Tuning(NeurIPS 2023,Oral) [Paper] [HF]Haotian Liu*,Chunyuan Li*,Qingyang Wu,Yong Jae Lee(*Equal Contribution)Release[1/30] 🔥 LLaVA-1.6 is out! With additional scaling to LLaVA-1.5, LLaVA-1.6-34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before. Check out theblog post, and explore thedemo! Models are available inModel Zoo. Training/eval data and scripts coming soon.[11/10]LLaVA-Plusis released: Learning to Use Tools for Creating Multimodal Agents, with LLaVA-Plus (LLaVA that Plug and Learn to Use Skills). [Project Page] [Demo] [Code] [Paper][11/2]LLaVA-Interactiveis released: Experience the future of human-AI multimodal interaction with an all-in-one demo for Image Chat, Segmentation, Generation and Editing. [Project Page] [Demo] [Code] [Paper][10/26] 🔥 LLaVA-1.5 with LoRA achieves comparable performance as full-model finetuning, with a reduced GPU RAM requirement (ckpts,script). We also provide adocon 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][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 thetechnical report, and explore thedemo! Models are available inModel Zoo. The training data and scripts of LLaVA-1.5 are releasedhere, and evaluation scripts are releasedhere![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][9/22]LLaVAis accepted by NeurIPS 2023 asoral presentation, andLLaVA-Medis accepted by NeurIPS 2023 Datasets and Benchmarks Track asspotlight presentation.More[11/6] SupportInteldGPU and CPU platforms.More details here.[10/12] LLaVA is now supported inllama.cppwith 4-bit / 5-bit quantization support![10/11] The training data and scripts of LLaVA-1.5 are releasedhere, and evaluation scripts are releasedhere![10/10]Roboflow Deep Dive: First Impressions with LLaVA-1.5.[9/20] We summarize our empirical study of training 33B and 65B LLaVA models in anote. 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''.[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 releaseLLaVA Benchfor 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 outLLaVA-from-LLaMA-2, and ourmodel zoo![6/26]CVPR 2023 TutorialonLarge Multimodal Models: Towards Building and Surpassing Multimodal GPT-4!  Please check out [Slides] [Notes] [YouTube] [Bilibli].[6/11] We released the preview for the most requested feature: DeepSpeed and LoRA support!  Please see documentationshere.[6/1] We releasedLLaVA-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 thepaperandpage.[5/6] We are releasingLLaVA-Lighting-MPT-7B-preview, based on MPT-7B-Chat!  Seeherefor more details.[5/2] 🔥 We are releasing LLaVA-Lighting!  Train a lite, multimodal GPT-4 with just $40 in 3 hours!  Seeherefor 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 outhere.[4/17] 🔥 We releasedLLaVA: Large Language and Vision Assistant. We propose visual instruction tuning, towards building large language and vision models with GPT-4 level capabilities.  Checkout thepaperanddemo.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 theOpenAI Terms of Usefor the dataset and the specific licenses for base language models for checkpoints trained using the dataset (e.g.Llama community licensefor 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.ContentsInstallLLaVA WeightsDemoModel ZooDatasetTrainEvaluationInstallIf you are not using Linux, doNOTproceed, see instructions formacOSandWindows.Clone this repository and navigate to LLaVA foldergitclonehttps://github.com/haotian-liu/LLaVA.gitcdLLaVAInstall Packagecondacreate-nllavapython=3.10-y
condaactivatellava
pipinstall--upgradepip# enable PEP 660 supportpipinstall-e.Install additional packages for training casespip install -e ".[train]"
pip install flash-attn --no-build-isolationUpgrade to latest code basegitpull
pipinstall-e.Quick Start With HuggingFaceExample Codefromllava.model.builderimportload_pretrained_modelfromllava.mm_utilsimportget_model_name_from_pathfromllava.eval.run_llavaimporteval_modelmodel_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 theload_pretrained_modelfunction inllava/model/builder.py.You can also use theeval_modelfunction inllava/eval/run_llava.pyto get the output easily. By doing so, you can use this code on Colab directly after downloading this repository.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)LLaVA WeightsPlease check out ourModel Zoofor all public LLaVA checkpoints, and the instructions of how to use the weights.DemoTo run our demo, you need to prepare LLaVA checkpoints locally.  Please follow the instructionshereto download the checkpoints.Gradio Web UITo 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 serverONCE.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")

    %% 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
    endLaunch a controllerpython-mllava.serve.controller--host0.0.0.0--port10000Launch a gradio web server.python-mllava.serve.gradio_web_server--controllerhttp://localhost:10000--model-list-modereloadYou 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 workerThis is the actualworkerthat performs the inference on the GPU.  Each worker is responsible for a single model specified in--model-path.python-mllava.serve.model_worker--host0.0.0.0--controllerhttp://localhost:10000--port40000--workerhttp://localhost:40000--model-pathliuhaotian/llava-v1.5-13bWait 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--controllerthe same, and modify the--portand--workerto a different port number for each worker.python-mllava.serve.model_worker--host0.0.0.0--controllerhttp://localhost:10000--port<differentfrom40000,say40001>--workerhttp://localhost:<changeaccordingly,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--deviceflag:--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 withCUDA_VISIBLE_DEVICES. Below is an example of running with the first two GPUs.CUDA_VISIBLE_DEVICES=0,1python-mllava.serve.model_worker--host0.0.0.0--controllerhttp://localhost:10000--port40000--workerhttp://localhost:40000--model-pathliuhaotian/llava-v1.5-13bLaunch 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-4bitor--load-8bitto themodel workercommand that you are executing. Below is an example of running with 4-bit quantization.python-mllava.serve.model_worker--host0.0.0.0--controllerhttp://localhost:10000--port40000--workerhttp://localhost:40000--model-pathliuhaotian/llava-v1.5-13b--load-4bitLaunch 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 havelora-mergein 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 themodel zoo.python-mllava.serve.model_worker--host0.0.0.0--controllerhttp://localhost:10000--port40000--workerhttp://localhost:40000--model-pathliuhaotian/llava-v1-0719-336px-lora-vicuna-13b-v1.3--model-baselmsys/vicuna-13b-v1.3CLI InferenceChat 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.python-mllava.serve.cli\--model-pathliuhaotian/llava-v1.5-7b\--image-file"https://llava-vl.github.io/static/images/view.jpg"\--load-4bitTrainBelow is the latest training configuration for LLaVA v1.5. For legacy models, please refer to README ofthisversion 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 afrozen pretrainedvision encoder to afrozen 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 theper_device_train_batch_sizeand increase thegradient_accumulation_stepsaccordingly. Always keep the global batch size the same:per_device_train_batch_sizexgradient_accumulation_stepsxnum_gpus.HyperparametersWe use a similar set of hyperparameters as Vicuna in finetuning.  Both hyperparameters used in pretraining and finetuning are provided below.PretrainingHyperparameterGlobal Batch SizeLearning rateEpochsMax lengthWeight decayLLaVA-v1.5-13B2561e-3120480FinetuningHyperparameterGlobal Batch SizeLearning rateEpochsMax lengthWeight decayLLaVA-v1.5-13B1282e-5120480Download 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 paperhere.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.--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.Pretrain takes around 20 hours for LLaVA-7B on 8x V100 (32G)We provide training script with DeepSpeedhere.
Tips:If you are using V100 which is not supported by FlashAttention, you can use thememory-efficient attentionimplemented inxFormers. Install xformers and replacellava/train/train_mem.pyabove withllava/train/train_xformers.py.Visual Instruction TuningPrepare dataPlease download the annotation of the final mixture our instruction tuning datallava_v1_5_mix665k.json, and download the images from constituting datasets:COCO:train2017GQA:imagesOCR-VQA:download script,we save all files as.jpgTextVQA:train_val_imagesVisualGenome:part1,part2After 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_2Start training!You may download our pretrained projectors inModel Zoo. 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.If you are do not have enough GPU memory:Use LoRA:finetune_lora.sh. We are able to fit 13B training in 8-A100-40G/8-A6000, and 7B training in 8-RTX3090. Make sureper_device_train_batch_size*gradient_accumulation_stepsis the same as the provided script for best reproducibility.Replacezero3.jsonwithzero3_offload.jsonwhich 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 outFinetune_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.EvaluationIn 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.SeeEvaluation.md.GPT-assisted EvaluationOur 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.Generate LLaVA responsespythonmodel_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.jsonlEvaluate the generated responses.  In our case,answer-file-ref.jsonlis the response generated by text-only GPT-4 (0314), with the context captions/boxes provided.OPENAI_API_KEY="sk-***********************************"pythonllava/eval/eval_gpt_review_visual.py\--questionplayground/data/coco2014_val_qa_eval/qa90_questions.jsonl\--contextllava/eval/table/caps_boxes_coco2014_val_80.jsonl\--answer-list\/path/to/answer-file-ref.jsonl\/path/to/answer-file-our.jsonl\--rulellava/eval/table/rule.json\--output/path/to/review.jsonSummarize the evaluation resultspythonsummarize_gpt_review.pyCitationIf you find LLaVA useful for your research and applications, please cite using this BibTeX:@misc{liu2024llava16,title={LLaVA-1.6: Improved reasoning, OCR, and world knowledge},url={https://llava-vl.github.io/blog/2024-01-30-llava-1-6/},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},}AcknowledgementVicuna: the codebase we built upon, and our base model Vicuna-13B that has the amazing language capabilities!Related ProjectsInstruction Tuning with GPT-4LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One DayOtter: In-Context Multi-Modal Instruction TuningFor future project ideas, please check out:SEEM: Segment Everything Everywhere All at OnceGrounded-Segment-Anythingto detect, segment, and generate anything by marryingGrounding DINOandSegment-Anything.