Official implementation of 'LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention' and 'LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model'.

This repo proposes LLaMA-Adapter (V2), a lightweight adaption method for fine-tuning Instruction-following and Multi-modal LLaMA models 🔥.

Try out the web demo 🤗 of LLaMA-Adapter: , LLaMA-Adapter V2 and ImageBind-LLM.

Join us at Wechat!

• [2024.01.18] LLaMA-Adapter is accepted by ICLR 2024!🎉
• [2023.12.08] We release OneLLM which aligns eight modalities to language using a unified framework.🔥🔥🔥
• [2023.11.11] We release SPHINX, a new multi-modal LLM, which is a huge leap from LLaMa-Adapter-V2.🔥🔥🔥
• [2023.10.11] We realse LLaMA-Adapter V2.1, an improved version of LLaMA-Adapter V2 with stronger multi-modal reasoning performance. Check llama_adapter_v2_multimodal7b for details.
• [2023.08.28] We release quantized LLM with OmniQuant, which is an efficient, accurate, and omnibearing (even extremely low bit) quantization algorithm. Multimodal version is coming soon.🔥🔥🔥
• [2023.07.24] We release LLaMA2-Accessory, an open-source toolkit for pre-training, fine-tuning and deployment of Large Language Models (LLMs) and mutlimodal LLMs. Please check Alpha-VLLM/LLaMA2-Accessory for more details!🔥🔥🔥
• [2023.07.05] We release the pretrain/finetune code of llama_adapter_v2_multimodal7b.
• [2023.07.04] We release the code for reproducing Gorilla by both full finetune and LLaMA-Adapter, please see gorilla/README.md.
• [2023.06.08] We release the demo of ImageBind-LLM.
• [2023.06.06] We release Point-Bind to extend ImageBind with 3D point clouds, which achieves 3D instruction-following capacity for imagebind_LLM.
• [2023.06.05] We support the integration of LLaMA-Adapter (both V1 and V2) and LangChain. Check out the Notebook.
• [2023.05.29] We release the code of ImageBind-LLM at imagebind_LLM.
• [2023.05.23] We release the demos and multi-modal code of LLaMA-Adapter V2!
• [2023.05.05] We release the paper and code of our new work Personalize Segment Anything, which efficiently fine-tunes Segment Anything with 10 seconds, and improves DreamBooth for better text-to-image generation.
• [2023.04.30] We noticed that GPT-4 evaluation has a strong positional bias in favor of the first response. We will soon update the paper to reveal the position bias. Great thanks to Canwen Xu.
• [2023.04.28] We release LLaMA-Adapter V2, a multi-modal instruction model. Check out our paper, demos and code!
• [2023.03.28] The paper and training code for LLaMA-Adapter V1 are released. 📌

• [P] means Pre-train and [F] means Fine-tune
• Image-Text-V1 is a concatenation of LAION400M, COYO, MMC4, SBU, Conceptual Captions, and COCO
• ImageBind Modalities include image, video, text, audio, depth, thermal, IMU
• ImageBind-dialog will be release soon

Efficiency Comparison:

By inserting adapters into LLaMA's transformer, our method only introduces 1.2M learnable parameters, and turns a LLaMA into an instruction-following model within 1 hour. For stablizing training at early stages, we propose a novel Zero-init Attention with zero gating mechanism to adaptively incorporate the instructional signals. After fine-tuning, LLaMA-Adapter can generate high-quality instruction-following sentences, comparable to the fully fine-tuned Stanford Alpaca and Alpaca-Lora.

Our approach can be simply extended to Multi-modal Input Instructions. The reasoning framework of image-conditioned LLaMA-Adapter for ScienceQA is as follows, which is also shared by other modalities, such as audio and video.

Here is a from-scratch script for LLaMA-Adapter V1.

conda create -n llama_adapter -y python=3.8
conda activate llama_adapter

# install pytorch
conda install pytorch cudatoolkit -c pytorch -y

# install dependency and llama-adapter
pip install -r requirements.txt
pip install -e .

Note: To setup other models, please refer to llama_adapter_v2_chat65b, llama_adapter_v2_multimodal7b and imagebind_LLM for more details.

Please request access to the pre-trained LLaMA from this form (official) or download the LLaMA-7B from Hugging Face (unofficial). Then, obtain the weights of our LLaMA-Adapter from here. We denote the path to the downloaded weights of LLaMA and adapters as TARGET_FOLDER and ADAPTER_PATH.

Here is an example to generate instruction-following sentences with 7B LLaMA model and our LLaMA-Adapter:

torchrun --nproc_per_node 1 example.py \
         --ckpt_dir $TARGET_FOLDER/model_size\
         --tokenizer_path $TARGET_FOLDER/tokenizer.model \
         --adapter_path $ADAPTER_PATH

We release the simple fine-tuning code of LLaMA-Adapter on LLaMA-7B model at here, which is for effortless reproduction with minimal dependencies. We will soon release the fine-tuning code for LLaMA-65B and multi-model LLaMA-Adapter.

Please download the 52K instruction-following training data from Standford Alpaca, and put it under DATA_PATH. Then run:

cd alpaca_finetuning_v1

torchrun --nproc_per_node 8 finetuning.py \
         --model Llama7B_adapter \
         --llama_model_path $TARGET_FOLDER/ \
         --data_path $DATA_PATH/alpaca_data.json \
         --adapter_layer 30 \
         --adapter_len 10 \
         --max_seq_len 512 \
         --batch_size 4 \
         --epochs 5 \
         --warmup_epochs 2 \
         --blr 9e-3 \
         --weight_decay 0.02 \
         --output_dir ./checkpoint/

How can I develop my critical thinking skills? Give me three suggestions.

• LLaMA-Adapter V2:

1. Read widely: Reading books, articles, and other sources of information can help you develop your critical thinking skills. By exposing yourself to different perspectives and ideas, you can learn to analyze and evaluate information more effectively.

2. Practice mindfulness: Mindfulness is the practice of being present in the moment and paying attention to your thoughts and feelings. By being more aware of your thoughts and emotions, you can better understand and evaluate them. This can help you develop a more objective and rational approach to thinking.

3. Engage in open-minded discussions: Join a discussion group or debate club where you can engage in open-minded discussions with others. This can help you develop your critical thinking skills by challenging your assumptions and encouraging you to consider different perspectives.

• LLaMA-Adapter V1:

1. Read widely and critically.
2. Engage in discussions with people who have different perspectives.
3. Ask questions and seek out answers.

As a superhero, how would you explain your origin story to a curious child?

• LLaMA-Adapter V2:

As a superhero, I have a very interesting and exciting origin story to share with you. Once upon a time, there was a young boy named John who lived in a small town. John was always curious and wanted to help others, so he spent his days exploring the world around him and learning about different cultures and people. One day, while he was out exploring, he stumbled upon a mysterious cave. Inside the cave, John found a strange device that had the power to give him incredible abilities. With these abilities, John could fly, shoot lasers from his eyes, and even turn invisible.

John knew that he had to use these powers for good, so he decided to become a superhero and use his abilities to protect his town and the people who lived there. He started to train every day, learning how to use his powers and how to fight against evil. Eventually, John became the superhero known as "The Invisible Man" and he used his powers to help others and make the world a better place.

That's my origin story, and I'm sure you'll love it!

• LLaMA-Adapter V1:

Once upon a time, there was a young boy who had a special power. He used his power to help people in need and protect the innocent. He became known as the superhero, and he used his powers to fight evil and protect the world.

Tell me about alpacas.

• LLaMA-Adapter: Alpacas are members of the camelid family and are native to the Andes Mountains of South America. They are typically found in herds of 10-20 animals and are known for their gentle nature and their ability to thrive in harsh climates. Alpacas are typically white or grey in color and have long, silky coats. They are also known for their ability to produce high-quality wool, which is used in the production of clothing and other products.

Tell me about the president of Mexico in 2019.

• LLaMA-Adapter: The president of Mexico in 2019 was Andrés Manuel López Obrador, who took office on December 1st, 2018. He is a member of the left-wing party Morena and is known for his populist policies. He has focused on tackling corruption, reducing poverty, and improving the country's infrastructure. He has also been a vocal critic of the United States' immigration policies.

Write a Python program that prints the first 10 Fibonacci numbers.

• LLaMA-Adapter:

def fibonacci():
    a = 0
    b = 1
    for i in range(10):
        a, b = b, a + b
        print(a, b)
    print("Fibonacci numbers: ", a, b)

fibonacci()

def fibonacci(n):
    if n == 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fibonacci(n-1) + fibonacci(n-2)

print(fibonacci(10))

numbers = [0, 1]

for i in range(2, 11):
    numbers.append(numbers[i-2] + numbers[i-1])

print(numbers)
# Output: [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

# Fibonacci numbers are the numbers in the following integer sequence, called the Fibonacci sequence, and characterized by the fact that every number after the first two is the sum of the two preceding ones.

# Fibonacci sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...

# Program:

# Initialize the first two numbers of the Fibonacci sequence
a = 0
b = 1

# Print the first 10 numbers of the Fibonacci sequence
print(a)
print(b)

for i in range(2,10):
    c = a + b
    a = b
    b = c
    print(c)

Write a program that prints the numbers from 1 to 100. But for multiples of three print 'Fizz' instead of the number and for the multiples of five print 'Buzz'. For numbers which are multiples of both three and five print 'FizzBuzz'.

• LLaMA-Adapter:

def print_numbers(n):
    for i in range(1, 101):
        if i % 3 == 0 and i % 5 == 0:
            print("FizzBuzz")
        elif i % 3 == 0:
            print("Fizz")
        elif i % 5 == 0:
            print("Buzz")
        else:
            print(i)

for i in range(1, 101):
    if i % 3 == 0 and i % 5 == 0:
        print("FizzBuzz")
    elif i % 3 == 0:
        print("Fizz")
    elif i % 5 == 0:
        print("Buzz")
    else:
        print(i)

for i in range(1, 101):
    if i % 3 == 0 and i % 5 == 0:
        print('FizzBuzz')
    elif i % 3 == 0:
        print('Fizz')
    elif i % 5 == 0:
        print('Buzz')
    else:
        print(i)

for (let i = 1; i <= 100; i++) {
  if (i % 3 === 0 && i % 5 === 0) {
    console.log("FizzBuzz");
  } else if (i % 3 === 0) {
    console.log("Fizz");
  } else if (i % 5 === 0) {
    console.log("Buzz");
  } else {
    console.log(i);
  }
}

Peng Gao, Jiaming Han, Renrui Zhang, Aojun Zhou

🔥 We are hiring interns, postdocs, and full-time researchers at the General Vision Group, Shanghai AI Lab, with a focus on multi-modality and vision foundation models. If you are interested, please contact gaopengcuhk@gmail.com.

If you find our LLaMA-Adapter code and paper useful, please kindly cite:

@article{zhang2023llamaadapter,
  title = {LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention},
  author={Zhang, Renrui and Han, Jiaming and Liu, Chris and Gao, Peng and Zhou, Aojun and Hu, Xiangfei and Yan, Shilin and Lu, Pan and Li, Hongsheng and Qiao, Yu},
  journal={arXiv preprint arXiv:2303.16199},
  year={2023}
}

If you find our LLaMA-Adapter V2 code and paper useful, please kindly cite:

@article{gao2023llamaadapterv2,
  title = {LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model},
  author={Gao, Peng and Han, Jiaming and Zhang, Renrui and Lin, Ziyi and Geng, Shijie and Zhou, Aojun and Zhang, Wei and Lu, Pan and He, Conghui and Yue, Xiangyu and Li, Hongsheng and Qiao, Yu},
  journal={arXiv preprint arXiv:2304.15010},
  year={2023}
}

This repo benefits from LLaMA, Stanford Alpaca, and Alpaca-Lora. Thanks for their wonderful works.