🌋 LLaVA: Large Language and Vision Assistant
Visual instruction tuning towards large language and vision models with GPT-4 level capabilities.
[📢 LLaVA-NeXT 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 Lee
Visual Instruction Tuning (NeurIPS 2023, Oral) [Paper] [HF]
Haotian Liu*, Chunyuan Li*, Qingyang Wu, Yong Jae Lee (*Equal Contribution)
Release
- [2024/05/10] 🔥 LLaVA-NeXT (Stronger) models are released, stronger LMM with support of LLama-3 (8B) and Qwen-1.5 (72B/110B). [Blog] [Checkpoints] [Demo] [Code]
- [2024/05/10] 🔥 LLaVA-NeXT (Video) is released. The image-only-trained LLaVA-NeXT model is surprisingly strong on video tasks with zero-shot modality transfer. DPO training with AI feedback on videos can yield significant improvement. [Blog] [Checkpoints] [Code]
- [03/10] Releasing LMMs-Eval, a highly efficient evaluation pipeline we used when developing LLaVA-NeXT. It supports the evaluation of LMMs on dozens of public datasets and allows new dataset onboarding, making the dev of new LMMs much faster. [Blog] [Codebase]
- [1/30] 🔥 LLaVA-NeXT (LLaVA-1.6) 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, and explore the demo! Models are available in Model Zoo. Training/eval data and scripts coming soon.
- [11/10] 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] [Demo] [Code] [Paper]
- [11/2] 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] [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 a doc 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]
- [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, and explore the demo! Models are available in Model Zoo. The training data and scripts of LLaVA-1.5 are released here, and evaluation scripts are released here!
- [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] LLaVA is accepted by NeurIPS 2023 as oral presentation, and LLaVA-Med is accepted by NeurIPS 2023 Datasets and Benchmarks Track as spotlight presentation.
More
- [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 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, and our model zoo!
- [6/26] CVPR 2023 Tutorial on Large 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 documentations here.
- [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 and page.
- [5/6] We are releasing LLaVA-Lighting-MPT-7B-preview, based on MPT-7B-Chat! See here for more details.
- [5/2] 🔥 We are releasing LLaVA-Lighting! Train a lite, multimodal GPT-4 with just $40 in 3 hours! See here 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.
- [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 and demo.
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 for the dataset and the specific licenses for base language models for checkpoints trained using the dataset (e.g. Llama community 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
If you are not using Linux, do NOT proceed, see instructions for macOS and Windows.
- Clone this repository and navigate to LLaVA folder
git clone https://github.com/haotian-liu/LLaVA.git
cd LLaVA
- Install Package
conda create -n llava python=3.10 -y
conda activate llava
pip install --upgrade pip # enable PEP 660 support
pip install -e .
- Install additional packages for training cases
pip install -e ".[train]"
pip install flash-attn --no-build-isolation
Upgrade to latest code base
git pull
pip install -e .
# if you see some import errors when you upgrade,
# please try running the command below (without #)
# pip install flash-attn --no-build-isolation --no-cache-dir
Quick Start With HuggingFace
Example Code
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.
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 Weights
Please check out our Model Zoo 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.
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
python -m llava.serve.controller --host 0.0.0.0 --port 10000
Launch a gradio web server.
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.
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.
# 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
Tokenizers (temporary): llava-hf/llava-1.5-7b-hf, llava-hf/llava-1.5-13b-hf, liuhaotian/llava-v1.6-34b-tokenizer.
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).
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.
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.
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.
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.
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.
python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhao
LLaVA 是一个开源的大型多模态聊天机器人：它是一个视觉语言模型，你可以给它看图片并和它对话，通过视觉指令微调构建，目标是达到 GPT-4V 级别的能力（NeurIPS 2023 Oral）。