Fine-Tuning Llama 3 to Create a Hindi Language Chatbot: Step-by-Step Guide

June 6, 2024

Our Aim in This Tutorial

In this guide we’ll demonstrate how to fine-tune Llama 3 on E2E Networks’s TIR platform.

TIR provides a ready-made no-code solution to deploying, fine-tuning, and inferencing LLMs. It allows users to upload their datasets into containers that can later be used for building AI workflows. Other features of TIR include pipelines, Gen AI APIs, and vector databases, all of which can be launched in a straightforward fashion using their UI.

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Overall TIR aims to simplify the management of computationally intensive operations performed by data scientists and AI/ML researchers.

Let’s Start Fine-Tuning

Let’s begin fine-tuning Llama 3. We’ll be using the hind_encorp dataset. This dataset contains English Text Snippets and their translation in Hindi. Here’s how it looks like:

{ "en": "They had justified their educational policy of concentrating on the education of a small number of upper and middle-class people with the argument that the new education would gradually ' filter down ' from above .", "hi": "कम संख़्या वाले उच्च एवं मध्यम श्रेणी के लोगों तक ही अपनी शिक्षा नीति को केंद्रित करने को इस तर्क के साथ न्यायसंगत बताया कि नयी शिक्षा Zक्रमश : ऊपर से नीचे की ओर छनते हुए जायेगी ." }

We’ll use this dataset to enhance the Hindi translation abilities of our Llama 3 Model.

The TIR platform expects the dataset to be in JSONL format. So, first, let’s convert our dataset into that format.


import pandas as pd
import json
from datasets import load_dataset


data = load_dataset('hind_encorp', split= 'train')
df = data.to_pandas()
# Example DataFrame creation or loading a DataFrame - not provided in your snippet
# Assuming df is your DataFrame and already loaded


# Sample 10% of the rows randomly
sampled_df = df.sample(frac=0.1)  # Adjust frac to change the fraction


# Function to extract values from the dictionary in the 'translation' column
def extract_values(row):
    return {'input': row['translation']['en'], 'output': row['translation']['hi']}


# Apply the function to create a new column for JSONL format in the sampled DataFrame
sampled_df['jsonl_format'] = sampled_df.apply(extract_values, axis=1)


# Write to JSONL file
with open('output.jsonl', 'w') as file:
    for item in sampled_df['jsonl_format']:
        json.dump(item, file)
        file.write('\n')

Now we will upload this data into an EOS bucket. On the TIR platform click on Dataset and then Create Dataset.

Once you’ve created a dataset, you can use the following Minio CLI commands to upload the JSONL file into an EOS bucket.

Now go to Foundation Studio and click on Fine Tune Models. Select Llama-3-8B as your model, and create a Hugging Face Integration token.

Select Dataset type as custom, and then choose a dataset. The ones that we uploaded earlier will be available in an EOS bucket.

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Now change the prompt configuration so as to align with the dataset.

In the next section, you can tune the hyperparameter configuration for the fine-tuning job. We’ll go with the default values:

Additionally, you can set up Wandb integration if you wish to monitor the fine-tuning process.

Select the GPU node you wish to use for running the fine-tuning.

With that set, you can launch the process.

Inference

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Once the fine-tuning is complete, your model will be ready to be deployed in the Fine Tune Models section of the Foundation Studio.

Go ahead and deploy the model. The deployed model will be available in the Model Endpoints section of the Inference tab.

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Click on the API request to receive your API key for the endpoint.

You can query the endpoint in the following manner:


import requests
import json


url = "https://infer.e2enetworks.net/project/p-2475/endpoint/is-1393/v1/models/finetuned-meta-llama-Llama-3-8b-hf:predict"


api_key = 'YOUR_API_KEY'
query = 'YOUR_QUERY'


payload = json.dumps({
    "instances": [
    {
        "text": f'{query}'
    }
    ]
})


headers = {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer ' + f'{api_key}'
}


response = requests.request("POST", url, headers=headers, data=payload)


response.text

Let’s try out a query:

“Translate the following English Text into Hindi: Experience the state-of-the-art performance of Llama 3, an openly accessible model that excels at language nuances, contextual understanding, and complex tasks like translation and dialogue generation. With enhanced scalability and performance, Llama 3 can handle multi-step tasks effortlessly, while our refined post-training processes significantly lower false refusal rates, improve response alignment, and boost diversity in model answers. Additionally, it drastically elevates capabilities like reasoning, code generation, and instruction following. Build the future of AI with Llama 3.”

Results:
'{"predictions":["Here is the translation of the provided English text into Hindi:

"लामा 3 के अत्याधुनिक प्रदर्शन का अनुभव करें, जो एक खुलकर सुलभ मॉडल है जो भाषा की बारीकियों, संदर्भिक समझ और अनुवाद तथा संवाद सृजन जैसे जटिल कार्यों में उत्कृष्ट है। बेहतर स्केलेबिलिटी और प्रदर्शन के साथ, लामा 3 आसानी से मल्टी-स्टेप कार्यों को संभाल सकता है, जबकि हमारी परिष्कृत पोस्ट-ट्रेनिंग प्रक्रियाएं गलत इनकार दरों को काफी कम करती हैं, प्रतिक्रिया संरेखण में सुधार करती हैं, और मॉडल उत्तरों में विविधता बढ़ाती हैं। इसके अतिरिक्त, यह तर्क करने की क्षमता, कोड जनरेशन, और निर्देशों का पालन करने जैसी क्षमताओं को भी उल्लेखनीय रूप से ऊंचा उठाता है। लामा 3 के साथ एआई के भविष्य का निर्माण करें।"

"]}'

Conclusion

In this blog, we have effectively tested how to create a Hindi-language chatbot on TIR. Go ahead and try it for yourself.

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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.

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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.

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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.
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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

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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.
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

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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.

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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.

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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.

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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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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