An Introduction to TinyLlama: A 1.1B Model Trained on 3 Trillion Tokens

October 17, 2023

The TinyLlama model is an interesting development in the space of Large Language Models (LLM), which is being pre-trained as I write this article. This model is a slimmed-down derivative of the open-source Llama2 LLM developed by Meta. TinyLlama has 1.1B parameters and aims to train on 3 trillion tokens within 90 days, where a token is essentially a unit of text that is used as input to an LLM model. The training commenced on 1 September 2023. This experiment, started by Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, and Wei Lu, has the potential to become a breakthrough in building capable LLMs using less computing power if completed successfully.

What Is the Motivation Behind TinyLlama?

Llama2, which is the precursor for TinyLlama, is a family of open-source LLM models for text generation. They also boast small models that can be run locally on Windows or Mac without the need for expensive GPUs. This was a major step forward in making LLMs generally available, including on devices with low computing power, such as mobile phones. The smallest version of Llama 2 has 7B parameters and was trained on 2T tokens, following the Chinchilla Scaling Law. The Chinchilla Scaling Law states that the number of tokens required to train a model should be roughly 20 times the number of parameters in the model. For example, a Llama 2 model with 7B parameters needs about 1.4T tokens for effective pre-training. It is claimed that the addition of tokens beyond the Chinchilla optimal data size will not yield significant improvements in model performance.

TinyLlama is an experiment designed to challenge exactly this claim. The 3T tokens to be used for pre-training vastly exceed the optimal size recommended by the Chinchilla scaling law. The idea is to find out if a model with fewer parameters can be trained to be more capable in language tasks by training it long enough on large amounts of data. This hypothesis is supported by the fact that the training metrics, such as accuracy and perplexity, did not reach saturation for Llama2 and previous LLM models when their pre-training was completed. As shown in the comparison plots taken from the Llama 2 paper, the training loss curves do not plateau even as they have consumed all the tokens. There is still room for improvement in these models by pre-training on additional data. However, the limiting factor is the compute budget, given their huge parameter sizes. In contrast, TinyLlama is a scaled-down model with relatively few parameters. This model is being run on 16 A100-40G GPUs, with an approximate budget of 40K USD. Thus, the model can afford to be trained on data of an enormous size, leaving a much smaller carbon footprint.

Model Architecture

The model has the same transformer architecture as Llama 2. Specifically, it uses Root Means Squared Norm (RMSNorm) ^[1] layer pre-normalization to normalize the intermediate layers in a transformer rather than just the output layer. Pre-normalization is done to improve model stability. The mode further uses the SwiGLU ^[2]activation function, Rotary Positional Embedding (RoPE)^[3], and Grouped Query Attention (GQA) ^[4] to improve model training performance. TinyLlama is trained on the GitHub-excluded subset of Slimpajama and Starcoderdata. To scale down the Llama2 architecture, the model uses 22 layers, 6 heads, an embedding dimension of 2048, and an intermediate dimension of 5632, leading to ~1.1B training parameters. The model implementation uses a bunch of optimization techniques such as fused layer norm, fused SwiGLU, and FlashAttention for speed-up, which achieved a throughput of 24K tokens per second on an A100-40G GPU.

Current Progress

The training progress of the model can be tracked live here. At the time of writing this article, the model has been trained on over 1T tokens and is running its second epoch. The model hit the IT token milestone on 3 October 2023. The cross-entropy training loss as well as the validation loss continue to decrease, with no visible signs of saturation. This shows that the model is training well and is not overfitting. Note that the model has already crossed the Chinchilla-optimal data size, which is 22B tokens (20 * 1.1B = 22B). The latest checkpoint for the base model and chat model after pre-training on 1T tokens has been released, as can be viewed on the model’s GitHub page. The authors also provide a Hugging Face library template script to install the model and interact with the chat interface. The model can be run for free on a Google Colab with a T4 GPU. I interacted with the chat interface on a Google Colab and found the results to be promising, as shown below:


!pip install transformers
!pip install accelerate


from transformers import AutoTokenizer
import transformers
import torch
model = "PY007/TinyLlama-1.1B-Chat-v0.3"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)


prompt = "Give me an easy and detailed recipe for making pancakes."
formatted_prompt = (
    f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
)




sequences = pipeline(
    formatted_prompt,
    do_sample=True,
    top_k=1,
    top_p = 0.9,
    num_return_sequences=1,
    repetition_penalty=1.1,
    max_new_tokens=1024,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

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As can be seen from the above script, an advantage of open-source LLMs is that the user has full control over the chat interface and can tune the prompts as desired.

Potential Use Cases

TinyLlama, being a small model, can be easily deployed on devices with limited memory and computing power. This could lead to novel end-user applications for mobile devices, such as chat dialogue generation in video games. An interesting use case of TinyLlama, which was verified by the authors, is the speculative decoding of large LLMs at inference, whereby the weights of a predicted sequence of tokens are looked up in advance to reduce the inference time latency.

Parting Thoughts

The outlook for the TinyLlama project is promising. If the model can successfully consume all 3T tokens without reaching saturation, it would provide the AI community with a deeper insight into LLM training. Even if the model saturates early on, we can probably gain insights into the phenomenon of saturation. However, what remains to be seen is how well the TinyLlama model compares in performance with Llama2 and other LLMs. If the gain in model performance is modest, then we need to consider if it is worth investing the time and resources in feeding additional data to a tiny model. To use an LLM effectively for a production-based chat application, the responses should be concise and accurate, and the inference latency should be low. The current pre-training report does not provide any pointers on how the model compares with previous LLMs on downstream tasks. This would be an essential next step to pursue to fully comprehend the potential of this model. Nevertheless, the model training in itself provides useful insights into the capabilities of tiny models and the potential of improving the learned patterns using more training data than what is suggested by the Chinchilla Scaling Law.

References

Here are some references that might be of interest to you as you delve deeper into this model:

[1] Root Mean Square Layer Normalization

[2] GLU Variants Improve Transformer

[3] RoFormer: Enhanced Transformer with Rotary Position Embedding

[4] GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

[5] Llama 2: Open Foundation and Fine-Tuned Chat Models

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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

October 18, 2022

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