Introduction

Foundation models are large AI models trained on a large amount of data. These models are more accurate than smaller models and can handle more complex tasks and datasets. More computing resources and time are required to train a foundation model. In most cases, the training of a foundation model is deployed on a large computing cluster. Foundation models are widely applied in fields, such as natural language processing (NLP), image recognition, and speech processing. GPT-3, for example, is a well-known foundation model that achieves high performance in natural language generation tasks.

The core capability of foundation models is that they can handle complex tasks and datasets with high accuracy. Foundation models can extract patterns that are hard for humans to identify from large amounts of data, and therefore get empowered in NLP, computer vision, and speech recognition. Foundation models can also be fine-tuned to cater to specific tasks. However, training a foundation model requires large amounts of computing resources and energy, which can be costly. Therefore, cost-effectiveness is a key factor in developing and deploying foundation models.

In recent years, foundation models have become more popular as computing power, data availability, and machine learning algorithms continue to advance. It has been more feasible to train complex models of a larger scale thanks to high data availability and the ability to distribute computing across thousands of machines. Today, foundation models can deliver top-notch results in a wide range of tasks and applications, including NLP, computer vision, and speech recognition. Many factors have contributed to the wide adoption of foundation models across industries, including the development of new machine learning algorithms, such as deep learning algorithms which can improve the efficiency of model training. Furthermore, the availability of pre-trained models, such as BERT and GPT-3, has made it easier for developers to integrate the state-of-the-art model into applications.

Principles

The core principle of foundation models is to use a large number of parameters to capture complex patterns in large datasets. These models use deep learning algorithms to learn layered representations of data by processing data through multiple layers of nonlinear transformations. During the training of a foundation model, the model extracts increasingly complex patterns from the input data at each layer. This way, the model can capture intricate patterns that are difficult to identify using more traditional machine learning methods. By training on large datasets, these models can learn to recognize subtle patterns and correlations in the data, and in turn deliver more accurate predictions and enhanced performance on complex tasks.

In the training process of a foundation model, model parameters are optimized based on large-scale datasets to minimize the prediction error. In most cases, the training process includes the following steps:

Data preprocessing: cleans, normalizes, and transforms input data to fit the model.
Initialization: randomly defines the initial values of model parameters or use pre-trained weights.
Forward propagation: feeds input data to the model in a forward direction and generates an output.
Backward propagation: processes the error between the predicted output and the actual output. In this step, optimization algorithms are used to update model parameters to make predictions more accurate.
Repetition: repeats forward propagation and backward propagation for several epochs to optimize model parameters and minimize the prediction error.

A trained model can be used to predict data. It generates predictions when it is fed with new input. The prediction does not affect existing parameters of the trained model. Whereas, it may involve additional data preprocessing so as to tailor the input data for the model. The accuracy and performance of the model can be evaluated by comparing its predictions with the actual output of a test dataset.

Algorithms

The core algorithm for a foundation model is usually a deep learning algorithm. Deep learning algorithms are a type of machine learning algorithm that uses multiple layers of artificial neural networks to learn and represent complex relationships in data. These algorithms have many hidden layers and can help trained models extract increasingly complex patterns from the input data at each layer, so as to capture intricate patterns more effectively. Using deep learning algorithms to train foundation models requires particular optimization algorithms, such as stochastic gradient descent, to optimize the model parameters based on the error between the predicted output and the actual output. There are many variations of deep learning algorithms, including convolutional neural networks, recurrent neural networks, and transformer models. The adoption of deep learning algorithms delivers state-of-the-art results across various applications, such as computer vision, NLP, and speech recognition.

Transformer

Compared with other algorithms, Transformer has the following advantages in NLP:

Attention mechanism: Transformer uses a self-attention mechanism that relates different positions of a single sequence to compute a representation of the same sequence and therefore can effectively capture long-range dependencies in the input sequence. This mechanism allows the model to better understand the context of each word in the sequence and improves the output quality of tasks such as machine translation and language modeling.
Parallel processing: Transformer is highly parallelizable and can take full advantage of modern hardware such as graphics processing units (GPUs) and tensor processing units (TPUs). It can be used to train large-scale models faster and more efficiently, and delivers better performance.
No recurrence: Unlike recurrent neural networks (RNN), Transformer is a non-recursive model, which is more memory-efficient and highly parallelizable.
Transfer learning: Transformer can pre-train a model on a large, unlabeled dataset and then fine-tunes the model on a smaller, labeled dataset to improve the performance of the model on specific tasks. This way, Transformer can leverage pre-training to enhance performance on various NLP tasks.

The Transformer algorithm has shown exceptional performance in a wide range of NLP tasks, including language translation, sentiment analysis, and text classification. Its advantages such as attention mechanism, parallel processing, and transfer learning capabilities, have made it a popular choice for foundation model applications in NLP.

The core principle of Transformer is the self-attention mechanism. The algorithm calculates the weighted sum of the input sequence, where the weights are learned based on the similarities between the elements in the sequence. This way, the self-attention mechanism allows the model to focus on different parts of the input sequence based on the importance of each element when the model processes the elements in the sequence.

The Transformer model consists of multiple layers of self-attention and feedforward neural networks. Each layer independently processes the input sequence, allowing the model to capture complex patterns in the data. The self-attention mechanism enables the model to effectively capture long-range dependencies in the input sequence, which is important for NLP tasks such as machine translation and text summarization.

The Transformer model also uses residual connections and layer normalization to improve its stability during training. Residual connections allow the model to learn the residual mapping between the input and output of each layer, which helps to prevent the vanishing gradient problem. Layer normalization reduces the impact of covariate shift during training and improves the performance of the model.

In short, the self-attention mechanism allows the Transformer model to effectively capture the intricate patterns in natural language data and deliver state-of-the-art performance on NLP tasks.

Open source foundation models

The following open source models are developed for foundation model applications:

ResNet: short for Residual Network. ResNet is a deep convolutional neural network architecture that is widely used in the image classification field. ResNet was introduced by Microsoft in 2015. ResNet won several image recognition competitions because of its capabilities in handling foundation and complex image datasets.
GPT-2: short for Generative Pretrained Transformer 2. GPT-2 is a transformer-based language model developed by OpenAI for natural language processing tasks, such as language translation, text summarization, and question answering. With its ability to generate coherent and high-quality text, GPT has become a popular choice for generating natural language responses in conversational AI applications.
BERT: short for Bidirectional Encoder Representations from Transformers. BERT is a transformer-based language model developed by Google for natural language processing tasks, such as question answering, sentiment analysis, and text classification. BERT is known for its capability to capture complex semantic relationships between words, making it a popular choice for NLP tasks that require a deep understanding of language context.
Faster R-CNN: a deep learning model used for object detection and localization in images. Faster R-CNN was developed by Microsoft in 2015, and is known for its capability to detect objects in images with high accuracy and speed, making it a popular choice for many fields including self-driving cars, surveillance systems, and robotics.
YOLO: short for You Only Look Once. YOLO is another deep learning model used for object detection in images. It was developed by Joseph Redmon in 2016, and is known for its capability to detect objects in real time with high accuracy and speed, making it a popular choice for real-time video analysis applications.

These are just a few of the many open source frameworks that can be used for creating foundation models. Each framework has its unique set of features and trade-offs. Make sure that you choose a framework that best suits your project needs.

Open source framework

The following open source frameworks are popular for building foundation models:

TensorFlow: an open source framework developed by Google for creating and training machine learning models, including foundation models. TensorFlow provides support for a wide range of applications, including computer vision, natural language processing, and speech recognition.
PyTorch: a popular open source framework developed by Facebook for creating foundation models. PyTorch is widely used in industrial and academic fields for building deep learning models like natural language processing models and computer vision models.
Caffe: an open source framework developed by the Berkeley Vision and Learning Center for creating deep learning models that are widely used in industrial and academic fields. Caffe supports both GPU-based and CPU-based computational kernel libraries.
MXNet: an open source deep learning framework developed by Amazon for creating foundation models that are used in industrial and academic fields. MXNet can be used for a wide range of applications, including computer vision, natural language processing, and speech recognition.
Keras: a high-level neural networks API written in Python that provides a user-friendly interface for creating and training foundation models. Keras is capable of running on top of TensorFlow or Theano.

Deployment process

The deployment process for a foundation model may vary for different projects, but there are some common steps.

Prepare the model. Before you deploy the model, you need to optimize the model. For example, you need to reduce the model size and optimize the processing speed. You also need to convert the model into a suitable format that can be used together with the deployment platform.
Choose a deployment platform. Several types of deployment platforms are available, including cloud-based platforms such as AWS and Google Cloud, on-premises platforms such as Docker, and edge devices such as Raspberry Pi and Jetson Nano. Choose your platform based on factors including the size of the model, the requirements for processing speed, and the cost of the deployment.
Develop a deployment application. The deployment application runs on the deployment platform and interact with the model. It may be used for developing a web application, a mobile application, or APIs that can be accessed by other applications.
Deploy the model: To deploy the model, you need to upload the model to the selected deployment platform and integrate the model with the deployment applications. The deployment process may involve setting up the necessary infrastructure, such as servers and databases, and configuring the deployment platform to run applications.
Monitor and optimize the deployment: After you deploy the model, you need to keep monitoring and optimizing the model to ensure that it is running as expected. You may need to monitor the processing speed, the accuracy of the results, and the resource usage, and adjust the settings of the model to optimize the performance.

In general, the deployment of a foundation model may be complex and time-consuming, but it is an important step to bring the model from development to production. To handle the complexity in deploying the model, you need a thorough deployment plan, a strong technical expertise, and a deep understanding of the project requirements.

How do I deploy an LLaMA model on Alibaba Cloud?

Preparations

Visit the Machine Learning Platform for AI instance product pageand create a Data Science Workshop (DSW) instance.
Select the GPU compute type and select the pai.medium.1xv100 resource type. This resource type has 8 cores, 32 GB of memory, and 1 NVIDIA Tesla V100 GPU that provides 16 GB of graphics memory.
Select the PyTorch v1.12 image.
Click OK. The instance is created in a few minutes.

Download the model

Log on to the Jupyterlab interface and open Terminal.
Run the following commands to download the source code of LLaMA:

git config --global http.version HTTP/1.1
git clone https://github.com/facebookresearch/llama.git

Run the following commands to download the model:

mkdir models
cd models
    
wget https://huggingface.co/nyanko7/LLaMA-7B/resolve/main/consolidated.00.pth
wget https://huggingface.co/nyanko7/LLaMA-7B/resolve/main/params.json
wget https://huggingface.co/nyanko7/LLaMA-7B/resolve/main/tokenizer.model
wget https://huggingface.co/nyanko7/LLaMA-7B/resolve/main/checklist.chk

Install the model

Run the following commands to install the model:

pip install -r requirements.txt
pip install -e .

Start the model

Run the following commands to start the model:

cd llama
vim example.py
    
max_seq_len: int = 256,
max_batch_size: int = 16
    
    
#     prompts = [
#         # For these prompts, the expected answer is the natural continuation of the prompt
#         "I believe the meaning of life is",
#         "Simply put, the theory of relativity states that ",
#         "Building a website can be done in 10 simple steps:\n",
#         # Few shot prompts: https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api
#         """Tweet: "I hate it when my phone battery dies."
# Sentiment: Negative
# ###
# Tweet: "My day has been 👍"
# Sentiment: Positive
# ###
# Tweet: "This is the link to the article"
# Sentiment: Neutral
# ###
# Tweet: "This new music video was incredibile"
# Sentiment:""",
#         """Translate English to French:
# 
# sea otter => loutre de mer
# 
# peppermint => menthe poivrée
# 
# plush girafe => girafe peluche
# 
# cheese =>""",
#     ]
#     results = generator.generate(
#         prompts, max_gen_len=256, temperature=temperature, top_p=top_p
#     )
# 
#     for result in results:
#         print(result)
#         print("\n==================================\n")
    print("\nCompleted \n")
    
    while True:
        print("\n==================================\n")
        prompt = input("Please input question：   ")
        if prompt == 'end':
            break
        prompts = [prompt]
        print("\n Your prompt:      ", prompt)
        results = generator.generate(prompts, max_gen_len=256, temperature=temperature, top_p=top_p)
        result = results[0]
        print("\n",result)
        print("\n==================================\n")

torchrun --nproc_per_node 1 ./llama/example.py --ckpt_dir models --tokenizer_path models/tokenizer.model

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