Fine-Tuning Your Own Personal Copilot

November 30, 2023

In the ever-evolving realm of programming and software development, the pursuit of efficiency and productivity has given rise to remarkable innovations. Among these innovations are code generation models like Codex, StarCoder, and Code Llama. These models have showcased their remarkable ability to generate code snippets that resemble human-written code, thereby highlighting their immense potential as valuable coding assistants.

Fine-Tuning Your Own Personal Copilot

The ability to fine-tune and adapt large-scale language models to specific tasks has become a game-changer. It allows us to harness the power of pre-trained models and customize them to our own needs. In this blog post, we'll explore the concept of fine-tuning and how it can be used to create your own personal copilot for various tasks, using examples and references from the field.

The Challenge of Full Fine-Tuning

Full fine-tuning of LLMs is a resource-intensive endeavor. The hardware requirements for full fine-tuning can be daunting, as exemplified by the numbers provided:

Weight: 2 bytes (Mixed-precision training)
Weight gradient: 2 bytes
Optimizer state when using Adam: 4 bytes for original FP32 weight + 8 bytes for first and second moment estimates
Cost per parameter adding all of the above: 16 bytes per parameter
For a massive model like bigcode/starcoder with 15.5 billion parameters, this adds up to 248GB of GPU memory.

Considering the huge memory requirements for storing intermediate activations, full fine-tuning would require a minimum of 4 A100 80GB GPUs. Such a set-up is not only costly but also hard to come by. So, the question arises: how can we fine-tune these models efficiently without such astronomical hardware requirements?

Parameter-Efficient Fine-Tuning (PEFT) with QLoRA

To tackle the resource constraints, a more efficient approach is required. This is where Parameter-Efficient Fine-Tuning (PEFT) comes into play, particularly when coupled with techniques like QLoRA. Here's how it works:

Trainable parameters are significantly reduced, with a much smaller proportion of the original model being updated.
In the case of bigcode/starcoder, the trainable parameters are only about 0.7% of the total.

This reduction in trainable parameters results in significantly lower memory requirements:

Base model weight: 7.755 GB
Adapter weight: 0.22 GB
Weight gradient: 0.12 GB
Optimizer state when using Adam: 1.32 GB
In total, only about 10GB of GPU memory is needed for fine-tuning.

This is a game-changer, as it means you can carry out fine-tuning on a single A100 40GB GPU, which is much more accessible. The reduced memory requirements are made possible by leveraging techniques like Flash Attention V2 and Gradient Checkpointing.

Flash Attention V2 and Gradient Checkpointing

To make the process even more memory-efficient, Flash Attention V2 and Gradient Checkpointing are employed. These techniques significantly reduce the memory required for intermediate activation checkpointing.

For QLoRA with Flash Attention V2 and Gradient Checkpointing, the memory occupied by the model on a single A100 40GB GPU is just 26GB, even with a batch size of 4.

For full fine-tuning using FSDP (Fully Sharded Data Parallel) along with Flash Attention V2 and Gradient Checkpointing, the memory occupied per GPU ranges between 70GB to 77.6GB, with a per_gpu_batch_size of 1.

This combination of techniques makes it possible to fine-tune large language models without the need for an array of high-end GPUs.

Efficient Training of Language Models to Fill-in-the-Middle

Introduction

Language models have evolved significantly in recent years, and they are now more capable than ever before. But what if you could customize your own personal copilot, a model that can assist you in a highly specialized manner?

Fine-Tuning for Specialized Skills

Fine-tuning language models is the process of training pre-existing models to perform specific tasks or possess new skills. Recent research has revealed that this approach can be highly effective, allowing models to acquire skills beyond their original capabilities with minimal additional training. One notable skill that has been explored is Fill-in-the-Middle (FIM) capabilities, where models can generate content in the middle of a document.

Understanding the FIM-for-Free Property

One significant finding is the concept of the ‘FIM-for-Free’ property. It suggests that with the same amount of computational resources, FIM models can achieve similar performance as traditional left-to-right models in terms of standard language modeling tasks while also excelling in FIM tasks. In essence, fine-tuning models with FIM does not compromise their original abilities, making it a cost-effective approach to impart new skills.

Choosing the Right FIM Hyperparameters

When fine-tuning models for FIM, several hyperparameters play a crucial role in achieving optimal performance:

Character-Level Spans: Applying FIM transformation at the character level is recommended. This level of granularity allows the model to generate sensible completions, even when the prefix and suffix end within the middle of a token.
Context-Level vs. Document-Level FIM: Context-level FIM consistently outperforms document-level FIM in a range of scenarios. However, document-level FIM may be preferred for its simplicity in some implementations.
FIM Rate: The research indicates that FIM rates between 50% and 90% are reasonable choices. Higher FIM rates can significantly enhance model capabilities without undermining left-to-right skills.

Enhancing Infilling Capabilities

Infilling is the task of generating content to complete gaps within a document, which can be particularly useful for coding and text generation tasks. While the research focused on single-slot in filling, there are various directions for improving infilling capabilities:

Smarter Span Selection: Selecting spans in a more meaningful way, such as based on syntax or semantics, can enhance infilling performance.
Steerable Generation: Providing more control over the style and content of in-filled text can improve the usability of fine-tuned models.
Multi-Slot Infilling: While the research primarily examined single-slot infilling, extending this to multi-slot infilling presents new challenges and opportunities for customization.

The Future of Fine-Tuning Language Models

The ability to fine-tune language models for specific skills or tasks has the potential to revolutionize how we interact with AI systems. By optimizing hyperparameters and developing smarter selection methods, we can create highly specialized personal copilots that seamlessly assist us in a variety of domains.

In conclusion, fine-tuning language models for specific capabilities, such as FIM, opens up exciting possibilities for customized AI assistance. As the research in this field continues to evolve, we can expect even more powerful and versatile language models that cater to our individual needs.

Tutorial

If you require extra GPU resources for the tutorials ahead, you can explore the offerings on E2E CLOUD. We provide a diverse selection of GPUs. To get one, head over to MyAccount, and sign up. Then launch a GPU node as is shown in the screenshot below:

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Make sure you add your ssh keys during launch, or through the security tab after launching.

Once you have launched a node, you can use VSCode Remote Explorer to ssh into the node and use it as a local development environment.

Personal Copilot - Fine-Tuned

In this tutorial, we will guide you through the process of setting up a Personal Copilot project on a GPU using Jupyter Notebook. We'll cover the essential steps, from checking GPU availability to running a deep learning training script. The tutorial assumes that you have a Jupyter Notebook environment with access to a GPU.

Prerequisites

Access to a Jupyter Notebook environment with GPU support.
Basic knowledge of Python and deep learning concepts.
Familiarity with Jupyter Notebook operations.

Step 1: Checking GPU Availability

To check if your Jupyter Notebook environment is connected to a GPU, you can use the following code snippet:


gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
if gpu_info.find('failed') >= 0:
  print('Not connected to a GPU')
else:
  print(gpu_info)

This code runs the nvidia-smi command to fetch GPU information. If it contains the word ‘failed’, it means you're not connected to a GPU.

Step 2: Cloning a GitHub Repository

To get the code and data for your deep learning project, you can clone a GitHub repository. Use the following command to clone a repository from GitHub:


!git clone https://github.com/pacman100/DHS-LLM-Workshop.git

Replace the GitHub repository URL with the one you want to clone.

Step 3: Preparing the Environment

Next, you need to set up the Python environment for your project. Execute the following commands:


!pip install packaging
!pip uninstall -y ninja && pip install ninja
!ninja --version
!echo $?

These commands install necessary Python packages and check the version of Ninja, a build system used for some deep learning libraries.

Step 4: Changing Directory

Navigate to the cloned GitHub repository using the %cd command:


%cd /content/DHS-LLM-Workshop

This command changes the current working directory to your project's directory.

Step 5: Updating the Repository

To ensure you have the latest code and data, run the following commands to pull the latest updates from the GitHub repository:


!git pull
!pip install -r requirements.txt

These commands fetch the latest code changes and install the required Python packages defined in the requirements.txt file.

Step 6: Installing Additional Libraries

Install any additional libraries your project might need. For example, you can install the flash-attn library using the following command:


!pip install flash-attn --no-build-isolation

This command installs the ‘flash-attn’ library without building isolations.

Step 7: Login to External Services

In some deep learning projects, you might need to log in to external services, such as Weights & Biases (WandB) and the Hugging Face Hub. Use the following commands to log in:


!wandb login --relogin


from huggingface_hub import notebook_login


notebook_login()

These commands log you into Weights & Biases and the Hugging Face Hub. Here you have to input your key which can be accessed from here: Weights & Biases (WandB) and Huggingface Hub.

Step 8: Training Your Model

Now, you're ready to train your deep learning model. Execute the following commands to start training.

Before you start training the model, please get access to bigcode/starcoderbase-1b. This is required in order to access the model. Once you get the access, you can resume again.


%cd personal_copilot/training/


!git lfs install
!git pull
!python train.py \
    --model_path "bigcode/starcoderbase-1b" \
    --dataset_name "smangrul/hf-stack-v1" \
    --subset "data" \
    --data_column "content" \
    --split "train" \
    --seq_length 2048 \
    --max_steps 2000 \
    --batch_size 8 \
    --gradient_accumulation_steps 2 \
    --learning_rate 5e-5 \
    --lr_scheduler_type "cosine" \
    --weight_decay 0.01 \
    --num_warmup_steps 30 \
    --eval_freq 100 \
    --save_freq 500 \
    --log_freq 25 \
    --num_workers 4 \
    --bf16 \
    --no_fp16 \
    --output_dir "starcoderbase1b-personal-copilot" \
    --fim_rate 0.5 \
    --fim_spm_rate 0.5 \
    --use_flash_attn

These commands run your training script with various configuration options. Modify the options to suit your project.

Congratulations! You've successfully set up and trained a Personal Copilot project on a GPU in a Jupyter Notebook environment. You can use these steps as a template for your deep learning projects.

Conclusion

Fine-tuning large language models for specific tasks doesn't have to be an overwhelming endeavor. With techniques like Parameter-Efficient Fine-Tuning (PEFT), QLoRA, Flash Attention V2 and Gradient Checkpointing, it's possible to achieve remarkable results with more accessible hardware. These advancements are making the power of large language models available to a broader range of users, opening up exciting possibilities for developing your own personal copilot in various domains.

References

Research Paper: Efficient Training of Language Models to Fill in the Middle

Hugging Face Reference: https://huggingface.co/bigcode/starcoderbase-1b

Hugging Face Blog: https://huggingface.co/blog/personal-copilot

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November 4, 2024

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.
CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels.

Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

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Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

This is a decorative image for: Constructing 3D objects through Deep Learning

October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

Highly calibrated cameras that take a photograph of the image from various angles.
Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website.

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Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
GAUDI also removes GANs pathologies like mode collapse and improved GAN.
GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
The scenes are thus synthesized by interpolation within the hidden space.
The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html

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Fine-Tuning Your Own Personal Copilot

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