Steps to Build and Fine-Tune a Foundational LLM with Mistral on TIR

May 6, 2024

The world of communication is undergoing a revolution driven by large language models (LLMs). These powerful AI models can hold conversations, generate creative text formats, and answer your questions in an informative way. This has exciting implications in various fields, from customer service chatbots to education and entertainment.

In this hands-on guide, we'll delve into building our own chatbot. We'll leverage the capabilities of the Mistral model and harness the power of the TIR AI Platform to train our chatbot effectively. By the end of this walkthrough, you'll have a foundational understanding of building chatbots with LLMs and be well on your way to creating your own interactive AI companion.

Building a Chatbot

E2E Network’s TIR AI platform integrates seamlessly with Jupyter Notebook, the data scientist's favorite tool.

To set up E2E Cloud TIR AI Jupyter Notebook, follow this link: https://www.e2enetworks.com/blog/how-to-use-jupyter-notebooks-on-e2e-networks.

Let’s Play

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E2E Cloud's Jupyter Notebook makes installation a breeze. Just use these magic commands to install LLaMA Factory and get your chatbot project on the cloud:

%rm -rf LLaMA-Factory

!git clone https://github.com/hiyouga/LLaMA-Factory.git

%cd LLaMA-Factory

%ls

!pip install .

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Next, import the necessary packages by executing:


from llmtuner import run_exp, ChatModel, export_model


import torchtorch.cuda.is_available()

E2E Cloud takes care of CUDA setup for you.

Fine-Tuning the Mistral Model for Question-Answering

We'll use LLaMA Factory to fine-tune the Mistral model specifically for the task of question-answering.

Dataset Playground

For this task, we'll leverage three datasets from Hugging Face to fine-tune our model: "identity," "alpaca_gpt4_en," and "alpaca_gpt4_zh." These datasets provide a solid foundation for question-answering tasks.


# Here we train the model and export it to a directory called "E2E_Mistral-7B-ChatBot"
run_exp(dict(
  stage="sft",
  do_train=True,
  model_name_or_path="mistralai/Mistral-7B-Instruct-v0.2",
  dataset="identity,alpaca_gpt4_en,alpaca_gpt4_zh",
  template="mistral",
  finetuning_type="lora",
  lora_target="all",
  output_dir="E2E_Mistral-7B-ChatBot",
  per_device_train_batch_size=4,
  gradient_accumulation_steps=4,
  lr_scheduler_type="cosine",
  logging_steps=10,
  save_steps=100,
  learning_rate=1e-4,
  num_train_epochs=5.0,
  max_samples=500,
  max_grad_norm=1.0,
  fp16=True,
))

Now that we've configured the core elements, let's delve into the world of hyperparameters. These are the control knobs that fine-tune the learning process of your chatbot. Feel free to experiment with them to personalize your chatbot's behavior and optimize its performance.

Key Hyperparameters

model_name_or_path: This critical hyperparameter dictates the base model you're using. In our case, it's set to "Mistral," but remember, you can explore other options within the Mistral family.
template: This hyperparameter defines the underlying architecture used for fine-tuning. Understanding the template's role can help you choose the most suitable one for your specific task.
dataset: Here, you specify the datasets you want to use for training.
fine_tune_type: This hyperparameter allows you to select between fine-tuning with LoRA or QLoRA. These are advanced techniques that LLaMA Factory offers to optimize training efficiency.

Beyond the Basics

We've highlighted some essential hyperparameters, but LLaMA Factory offers a wider range for you to explore. These include:

epochs: This hyperparameter controls the number of times the model iterates through the training data. Adjusting it can influence the model's learning and avoid overfitting.
save_steps: This hyperparameter determines how often the model's progress is saved during training. It allows you to track the training process and potentially revert to earlier stages if needed.
learning_rate: This hyperparameter governs the step size the model takes when updating its internal parameters during training. Setting an appropriate learning rate is crucial for achieving optimal performance.
output_dir: This hyperparameter specifies the directory where the fine-tuned model and training logs will be saved. Keeping track of these outputs is essential for analyzing the training process and evaluating the final model.

By understanding and adjusting these hyperparameters, you can transform your chatbot from a basic model into a powerful and customized AI companion.

The training process can take some time to complete. The exact duration depends on several factors:

GPU Power: The processing muscle of your GPU significantly impacts training speed.
Model Architecture: The complexity of the chosen model architecture also plays a role. Simpler models generally train faster than their more intricate counterparts.
Hyperparameter Tuning: The hyperparameters you've configured can influence training time. Optimizing these settings can sometimes lead to faster training without sacrificing accuracy.

Once the training is complete, it's time to preserve your creation. LLaMA Factory conveniently stores checkpoints of the trained model within a dedicated folder inside its directory (‘output_dir’ hyperparameter).

The following code snippet allows you to effortlessly export your fine-tuned model to a directory named "E2E-ChatBot" within your E2E Cloud workspace. This way, you have a readily accessible copy of your chatbot, ready for further use and exploration.


export_model(dict(
  model_name_or_path="mistralai/Mistral-7B-Instruct-v0.2",
  adapter_name_or_path="E2E_Mistral-7B-ChatBot",
  finetuning_type="lora",
  template="mistral",
  export_dir="E2E-ChatBot",
))

You've successfully trained and saved your very own chatbot. Now, it's time to unleash its potential.


E2E_Chat_Bot = ChatModel(
    dict(
  model_name_or_path="mistralai/Mistral-7B-Instruct-v0.2",
  adapter_name_or_path="E2E_Mistral-7B-ChatBot",
  finetuning_type="lora",
  template="mistral",
))


messages = []
while True:
  query = input("\nUser: ")
  if query.strip() == "exit":
    break
  if query.strip() == "clear":
    messages = []
    continue


  messages.append({"role": "user", "content": query})
  print("E2E_Chat_Bot: ", end="", flush=True)
  response = ""
  for new_text in E2E_Chat_Bot.stream_chat(messages):
    response += new_text
  for r in response.split('.'):
    print(r.strip(), flush=True)
  print()
  messages.append({"role": "assistant", "content": response})

The provided code snippet acts as a bridge between you and your creation. Simply type your questions or prompts, and the chatbot will analyze them, using its fine-tuned knowledge to craft informative and engaging responses.

Here are some handy commands to keep in mind:

Ask questions, give it instructions, or simply have a conversation. The more you interact, the better it may understand your preferences and communication style.
Start fresh (clear): If you feel the conversation has gone off track, use the clear command. This will reset the chatbot's internal state, giving you a clean slate for a new interaction.
Exit the conversation (exit): When you're done exploring your chatbot's capabilities, use the exit command to conclude the session.

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Here is an example conversation from the awesome chatbot we have built:

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User: Hi

E2E_Chat_Bot: Hello! How can I help you today?

User: Do you know about E2E cloud network?

E2E_Chat_Bot: Yes, I am familiar with E2E Cloud Network

E2E Cloud Network is a technology that allows companies to host their applications and services in the cloud, allowing them to easily access and manage their resources and applications from anywhere in the world

It is a powerful solution that enables organizations to leverage the capabilities of the cloud to improve their operations and growth

User: can it also be used to train and deploy AI models?

E2E_Chat_Bot: Yes, E2E Cloud Network can be used to train and deploy AI models

In fact, it is a popular choice for organizations that want to deploy AI models in the cloud, as it enables them to easily scale and deploy their models to meet the needs of their users

E2E Cloud Network allows for easy deployment of AI models, with features such as containerization, automatic scaling, and automatic deployment, which can help organizations to reduce the time and effort required to deploy and manage their AI models

User: exit

GitHub

Congratulations! You've successfully built your very own chatbot.

The GitHub code used can be found at: https://github.com/Lord-Axy/Arrticle-Chat-Bot/blob/master/code.ipynb

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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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Communicate with your customers

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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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It can also help in completing DNA sequences.

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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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Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
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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

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

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An introduction to GAUDI

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What does GAUDI do?

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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.
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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.

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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