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Align-Anything aims to align any modality large models (any-to-any models), including LLMs, VLMs, and others, with human intentions and values. More details about the definition and milestones of alignment for Large Models can be found in AI Alignment. Overall, this framework has the following characteristics:

• Highly Modular Framework. Its versatility stems from the abstraction of different algorithm types and well-designed APIs, allowing users to easily modify and customize the code for different tasks.
• Support for Various Model Fine-Tuning. This framework includes fine-tuning capabilities for models such as LLaMA3.1, LLaVA, Gemma, Qwen, Baichuan, and others (see Model Zoo).
• Support Fine-Tuning across Any Modality. It supports fine-tuning alignments for different modality model, including LLMs, VLMs, and other modalities (see Development Roadmap).
• Support Different Alignment Methods. The framework supports different alignment algorithms, including SFT, DPO, PPO, and others.

	prompt Small white toilet sitting in a small corner next to a wall.	prompt A close up of a neatly made bed with two night stands	prompt A pizza is sitting on a plate at a restaurant.	prompt A girl in a dress next to a piece of luggage and flowers.
Before Alignment (Chameleon-7B)
After Alignment (Chameleon 7B Plus)

Alignment fine-tuning can significantly enhance the instruction-following capabilities of large multimodal models. After fine-tuning, Chameleon 7B Plus generates images that are more relevant to the prompt.

Algorithms

We support basic alignment algorithms for different modalities, each of which may involve additional algorithms. For instance, in the text modality, we have also implemented SimPO, KTO, and others.

Evaluation

We support evaluation datasets for Text -> Text, Text+Image -> Text and Text -> Image.

• ⚒️ : coming soon.

News

• 2024-10-10: We support SFT for Any -> Any modality models Emu3.
• 2024-09-24: We support SFT, DPO, RM and PPO for Text + Video -> Text modality models.
• 2024-09-13: We support SFT, DPO, RM and PPO for Text + Audio -> Text modality models.
• 2024-08-17: We support DPO and PPO for Text+Image -> Text+Image modality models.
• 2024-08-15: We support a new function in the evaluation module: the models_pk script in here, which enables comparing the performance of two models across different benchmarks.
• 2024-08-06: We restructure the framework to support any modality evaluation and the supported benchmark list is here.
• 2024-08-06: We support Text+Image -> Text+Image modality for the SFT trainer and Chameleon models.

Installation

# clone the repository
git clone [email protected]:PKU-Alignment/align-anything.git
cd align-anything

# create virtual env
conda create -n align-anything python==3.11
conda activate align-anything

• [Optional] We recommend installing CUDA in the conda environment and set the environment variable.

# We tested on the H800 computing cluster, and this version of CUDA works well. 
# You can adjust this version according to the actual situation of the computing cluster.

conda install nvidia/label/cuda-12.2.0::cuda
export CUDA_HOME=$CONDA_PREFIX

If your CUDA installed in a different location, such as /usr/local/cuda/bin/nvcc, you can set the environment variables as follows:

export CUDA_HOME="/usr/local/cuda"

Fianlly, install align-anything by:

pip install -e .

Wandb Logger

We support wandb logging. By default, it is set to offline. If you need to view wandb logs online, you can specify the environment variables of WANDB_API_KEY before starting the training:

export WANDB_API_KEY="..."  # your W&B API key here

Quick Start

Training Scripts

To prepare for training, all scripts are located in the ./scripts and parameters that require user input have been left empty. For example, the DPO scripts for Text + Image -> Text modality is as follow:

MODEL_NAME_OR_PATH="" # model path
TRAIN_DATASETS="" # dataset path
TRAIN_TEMPLATE="" # dataset template
TRAIN_SPLIT="" # split the dataset
OUTPUT_DIR=""  # output dir

source ./setup.sh # source the setup script

export CUDA_HOME=$CONDA_PREFIX # replace it with your CUDA path

deepspeed \
	--master_port ${MASTER_PORT} \
	--module align_anything.trainers.text_image_to_text.dpo \
	--model_name_or_path ${MODEL_NAME_OR_PATH} \
	--train_datasets ${TRAIN_DATASETS} \
	--train_template SPA_VL \
	--train_split train \
	--output_dir ${OUTPUT_DIR}

We can run DPO with LLaVA-v1.5-7B (HF format) and SPA-VL dataset using the follow script:

MODEL_NAME_OR_PATH="llava-hf/llava-1.5-7b-hf" # model path
TRAIN_DATASETS="sqrti/SPA-VL" # dataset path
TRAIN_TEMPLATE="SPA_VL" # dataset template
TRAIN_SPLIT="train" # split the dataset
OUTPUT_DIR="../output/dpo" # output dir
export WANDB_API_KEY="YOUR_WANDB_KEY" # wandb logging

source ./setup.sh # source the setup script

export CUDA_HOME=$CONDA_PREFIX # replace it with your CUDA path

deepspeed \
	--master_port ${MASTER_PORT} \
	--module align_anything.trainers.text_image_to_text.dpo \
	--model_name_or_path ${MODEL_NAME_OR_PATH} \
	--train_datasets ${TRAIN_DATASETS} \
	--train_template ${TRAIN_TEMPLATE} \
	--train_split ${TRAIN_SPLIT} \
	--output_dir ${OUTPUT_DIR}

Evaluation

All evaluation scripts can be found in the ./scripts. The ./scripts/evaluate.sh script runs model evaluation on the benchmarks, and parameters that require user input have been left empty. The corresponding script is as follow:

SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
cd "${SCRIPT_DIR}/../align_anything/evaluation" || exit 1

BENCHMARKS=("") # evaluation benchmarks
OUTPUT_DIR="" # output dir
GENERATION_BACKEND="" # generation backend
MODEL_ID="" # model's unique id
MODEL_NAME_OR_PATH="" # model path
CHAT_TEMPLATE="" # model template

for BENCHMARK in "${BENCHMARKS[@]}"; do
    python __main__.py \
        --benchmark ${BENCHMARK} \
        --output_dir ${OUTPUT_DIR} \
        --generation_backend ${GENERATION_BACKEND} \
        --model_id ${MODEL_ID} \
        --model_name_or_path ${MODEL_NAME_OR_PATH} \
        --chat_template ${CHAT_TEMPLATE}
done

For example, you can evaluate LLaVA-v1.5-7B (HF format) on POPE and MM-SafetyBench benchmarks using the follow script:

SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
cd "${SCRIPT_DIR}/../align_anything/evaluation" || exit 1

BENCHMARKS=("POPE" "MM-SafetyBench") # evaluation benchmarks
OUTPUT_DIR="../output/evaluation" # output dir
GENERATION_BACKEND="vLLM" # generation backend
MODEL_ID="llava-1.5-7b-hf" # model's unique id
MODEL_NAME_OR_PATH="llava-hf/llava-1.5-7b-hf" # model path
CHAT_TEMPLATE="Llava" # model template

for BENCHMARK in "${BENCHMARKS[@]}"; do
    python __main__.py \
        --benchmark ${BENCHMARK} \
        --output_dir ${OUTPUT_DIR} \
        --generation_backend ${GENERATION_BACKEND} \
        --model_id ${MODEL_ID} \
        --model_name_or_path ${MODEL_NAME_OR_PATH} \
        --chat_template ${CHAT_TEMPLATE}
done

You can modify the configuration files for the benchmarks in this directory to suit specific evaluation tasks and models, and adjust inference parameters for vLLM or DeepSpeed based on your generation backend. For more details about the evaluation pipeline, refer to the here.

Inference

Interactive Client

python3 -m align_anything.serve.cli --model_name_or_path your_model_name_or_path

Interactive Arena

python3 -m align_anything.serve.arena \
    --red_corner_model_name_or_path your_red_model_name_or_path \
    --blue_corner_model_name_or_path your_blue_model_name_or_path

Report Issues

If you have any questions in the process of using align-anything, don't hesitate to ask your questions on the GitHub issue page, we will reply to you in 2-3 working days.

Citation

Please cite the repo if you use the data or code in this repo.

@misc{align_anything,
  author = {PKU-Alignment Team},
  title = {Align Anything: training all modality models to follow instructions with unified language feedback},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/PKU-Alignment/align-anything}},
}

License

align-anything is released under Apache License 2.0.