The Competitive Advantage of 100K Context Window in LLMs

October 3, 2023

What Are Context Windows?

Context windows in Large Language Models (LLMs) typically refer to how many tokens a model can accept as a prompt in one go. This number is an indication of how much information can be fed into the model at a time so that it can generate responses as per that information. This is an important metric as it can greatly influence the quality of the results a model can give or, in other words, how much information can be extracted from the model in a single go. Models like GPT-3 will accept 2000 tokens while GPT-4 can go up to 32,000.

Advantages of Large Context Windows

The size of the context window describes the amount of information the model can keep in mind to generate a response. As such, the size getting larger is what is generally preferred, i.e., the larger the context window, the better it gets.

Consider the case of a book. Suppose you wish to get answers to a particular question based on the contents of a book. With a lower context window you have three straightforward ways to do this (more like a brute force solution). Option 1 is to summarize the details of the book in a way that captures all the required contents along with the query within the context window size. Option 2 is to include the book itself in the original training dataset of the book, which is not exactly feasible. The third option is to frame a bunch of successive queries using various constraints and structures such that all the book details required for the task at hand can be fed in for reference. As evident, all these methods are pretty complicated and not exactly ideal. But if the context window was large enough, one could just feed in the details of the entire book into the model along with a query in a single go and not worry about anything else. This is exactly why we prefer a larger content window.

Why Don't All Models Have Large Context Windows?

Larger context windows are always more favorable. However, it's not exactly feasible per say. A large context window requires much higher computation during training and inference. In other words, training a model with a large context window is very expensive. And, without training a base model explicitly for a large context window, one can’t expect to use large context windows – i.e., fine-tuning a model trained on a small context window for larger ones won’t work. Hence the only way to do it is to train the base model itself for the larger context window. However, the relationship between context window size and computational expense in the conventional transformer architecture works out to have a quadratic relationship. This makes training larger and larger context window models in the conventional sense very expensive.

Open Source LLMs with Large Context Windows

MPT-7B (Mosaic Pretrained Transformer - 7 Billion)

MPT-7B models are a series of open source LLMs with a notable speciality: its 65K context length! The ability comes from the ALiBi paper which removes Position Sinusoidal Encoding at the bottom of the conventional transformer architecture with Attention with Linear Biases (ALiBi) at the attention head. This change accelerates training speeds and allows larger context windows to be trained. This modification allows MPT-7B to achieve a high context window length of 65k. This capability can then be used to fine-tune MPT-7B Base to create MPT-7B-StoryWriter-65k+. This model can fit in entire books like ‘The Great Gatsby’ into a single prompt and generate an epilogue of it. Also ALiBi allows StoryWriter to work with even longer content length than what it was trained on (65K), like up to 84K in some test cases.

Claude’s 100K Context Window

Claude by Anthropic has moved from a context window length of just 9K to 100k tokens. This corresponds to around 75,000 words in a single go. Claude can accept hundreds of pages of information from businesses and other applications to analyze and respond with the right result in mere seconds. For context, an average person might take 5+ hours to go through the same amount of tokens! ‘The Great Gatsby’ book was fed into Claude with just a single line changed and the model was able to figure it out in just 22 seconds. Claude’s larger context window makes it much more suited to answer complex queries than vector search-based approaches.

How Can a Large Context Window Help

Let's try out MosaicML’s mosaicml/mpt-7b-storywriter model in a Jupyter Notebook.

Set up your environment and install the libraries.


!pip install -q -U bitsandbytes
!pip install -q -U git+https://github.com/huggingface/transformers.git
!pip install -q -U git+https://github.com/huggingface/peft.git
!pip install -q -U git+https://github.com/huggingface/accelerate.git
!pip install -q -U einops
!pip install xformers
!pip install scipy

Import the libraries.


from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
import transformers

Import the model after setting the context window size to 83968.


model_name = "mosaicml/mpt-7b-storywriter"


config = transformers.AutoConfig.from_pretrained(model_name, trust_remote_code=True)
config.max_seq_len = 83968 # (input + output) tokens can now be up to 83968
model = AutoModelForCausalLM.from_pretrained(model_name, config=config, load_in_4bit=True, device_map="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")

Set up the pipeline for text generation.


pipe = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

Set up the pipeline for text generation.


great_gatsby = """
Chapter 1


In my younger and more vulnerable years my father gave
……
……
……
So we beat on, boats against the current, borne back
ceaselessly into the past.




Epilogue:
"""

(Note: We’ve added an extra ‘Epilogue’ at the end to facilitate epilogue generation as we push this prompt into the text generation model.)

‍

Generate the text.


sequences = pipe(
    great_gatsby,
    max_length=83968,
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

Run the code – and we can observe text generation of the epilogue of the book in action.

Conclusion

Large context windows are crucial when working with LLMs. It allows more and more information to be kept in the memory when generating responses. This, ultimately, eliminates the need to use vector databases and other techniques to achieve the same results. An infinite context window could possibly be the holy grail for any LLM. Such an architecture would be able to encompass all the information available to generate any response and will lead to more accurate and well-rounded results, thereby eliminating any human-made biases.

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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

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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.
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Reference Links

https://tongtianta.site/paper/68922

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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

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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.

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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.

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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

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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