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Cobra: Extending Mamba to Multi-modal Large Language Model for Efficient Inference

arXiv Model Demo

<img src="assets/cobra.png" width="200px">

(Generated by SDXL, prompt: A cute cobra with flamming lava in the background, cartoon style)

Release

[5/23] A fix for the prompt format, which modifies the abnormal behavior during generation, has been implemented. Make sure you use the newest checkpoint to adapt to the new prompt format!

[5/18] The evaluation code has been uploaded!

[3/27] An intuitive demo, showcasing the generation speed of our Cobra model and LLaVA v1.5. <img src="assets/cobra_demo.gif">

[3/24] Try our demo online!

[3/24] The training and inference codes are released.

[3/24] Our model weight is available now.

[3/21] The repository is created.

Installation | Usage | Pretrained Models | Training VLMs | License

Installation

This repository was built using Python 3.10, but should be backwards compatible with any Python >= 3.8. We require PyTorch 2.1 or greater installation instructions can be found here. This repository was developed and has been thoroughly tested with PyTorch 2.1.0 and Torchvision 0.16.0.

Once PyTorch has been properly installed, you can install this package locally via an editable installation (or via pip install git+https://github.com/h-zhao1997/cobra):

git clone https://github.com/h-zhao1997/cobra
cd cobra
pip install -e .

# install mamba and other packages
pip install packaging ninja
pip install "mamba-ssm<2.0.0"
pip install causal-conv1d

# Verify Ninja --> should return exit code "0"
ninja --version; echo $?

If you run into any problems during the installation process, please file a GitHub Issue.

Usage

Once installed, loading and running inference with pretrained cobra models is easy:

import requests
import torch

from PIL import Image
from pathlib import Path

from cobra import load

hf_token = Path(".hf_token").read_text().strip()
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# In case your GPU does not support bf16
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16

# Load a pretrained VLM (either local path, or ID to auto-download from the HF Hub) 
model_id = "cobra+3b"
vlm = load(model_id, hf_token=hf_token)
vlm.to(device, dtype=dtype)

# Download an image and specify a prompt
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png"
image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB")
user_prompt = "What is going on in this image?"

# Build prompt
prompt_builder = vlm.get_prompt_builder()
prompt_builder.add_turn(role="human", message=user_prompt)
prompt_text = prompt_builder.get_prompt()

# Generate!
generated_text = vlm.generate(
    image,
    prompt_text,
    use_cache=True,
    do_sample=True,
    temperature=0.4,
    max_new_tokens=512,
)

For a complete terminal-based CLI for interacting with our VLMs, check out scripts/generate.py.

Training VLMs

Pretraining Datasets

The LLaVa v1.5 Instruct Dataset can be downloaded by the automated download script in scripts/preprocess.py:

# Download the `llava-v1.5-instruct` (Instruct Tuning) Image and Language Data (includes extra post-processing)
python scripts/preprocess.py --dataset_id "llava-v1.5-instruct" --root_dir <PATH-TO-DATA-ROOT>

# (In case you also wish to download the explicit vision-language alignment data)
python scripts/preprocess.py --dataset_id "llava-laion-cc-sbu-558k" --root_dir <PATH-TO-DATA-ROOT>

LVIS-Instruct-4V and LRV-Instruct can also be downloaded by the scripts in scripts/additional-datasets.

Model Configuration & Training Script

Here's how you would train Cobra follow the training recipe in our paper across 8 GPUs on a single-node:

# Run from the root of the repository
torchrun --standalone --nnodes 1 --nproc-per-node 8 scripts/pretrain.py \
  --model.vision_backbone_id "dinosiglip-vit-so-384px" \
  --model.image_resize_strategy "resize-naive" \
  --model.llm_backbone_id "mamba-2.8b-zephyr" \
  --model.type "cobra+3b" \
  --model.finetune_global_batch_size 128 \
  --model.finetune_per_device_batch_size 8 \
  --dataset.type "llava-lvis4v-lrv"

Citation

If you find our code or models useful in your work, please cite our paper:

@article{zhao2024cobra,
      title={Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference}, 
      author={Han Zhao and Min Zhang and Wei Zhao and Pengxiang Ding and Siteng Huang and Donglin Wang},
      year={2024},
      eprint={2403.14520},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

License

This project is released under the MIT License

Acknowledgement

We build our project based on