OpenWebUI - Beginner's Tutorial

 

OpenWebUI Tutorial: Setting Up and Using Local Llama 3.2 with Ollama

Introduction

This tutorial provides a step-by-step guide to setting up OpenWebUI as a user-friendly interface for the Llama 3.2 model using Ollama. By the end of this tutorial, you will have a fully functional local AI chatbot running on your computer.

Prerequisites

• Basic knowledge of command-line usage
• Installed Docker Desktop
• Installed Ollama

Tutorial Duration: 1 Hour

Step 1: Install and Set Up Docker (10 min)

Docker allows us to run OpenWebUI easily.

1. Download Docker Desktop from here.
2. Install Docker and ensure it is running.
3. Open a terminal (Command Prompt/PowerShell/Terminal) and verify installation:

   docker --version
   

   If a version number appears, Docker is installed correctly.

Step 2: Install Ollama (5 min)

Ollama is required to run Llama 3.2 locally.

1. Download and install Ollama from https://ollama.com/download.
2. Open a terminal and check if it is installed correctly:

   ollama --version
   

Step 3: Download Llama 3.2 Model (10 min)

We now download the Llama 3.2 model for local use.

1. Open a terminal and run:

   ollama pull meta/llama3
   

2. Wait for the download to complete (this may take some time depending on internet speed).

Step 4: Start Ollama (5 min)

Once the model is downloaded, start Ollama.

1. Run the following command:

   ollama serve
   

2. This will start the local Ollama server.

Step 5: Install and Run OpenWebUI (15 min)

Now, we will install and start OpenWebUI using Docker.

1. Pull the OpenWebUI Docker image:

   docker pull ghcr.io/open-webui/open-webui:main
   

2. Run OpenWebUI with the following command:

   docker run -d --name openwebui -p 3000:3000 -v open-webui-data:/app/data --restart unless-stopped ghcr.io/open-webui/open-webui:main
   

3. Verify that OpenWebUI is running:

   docker ps
   

   If you see a container named openwebui, it is running.

Step 6: Access OpenWebUI (5 min)

Now, open the user interface in a web browser.

1. Go to http://localhost:3000 in your browser.
2. Create an account and log in.

Step 7: Configure OpenWebUI to Use Ollama (5 min)

1. Go to Settings → LLM Provider.
2. Select Ollama.
3. Enter the model name: llama3.
4. Save the settings.

Step 8: Test the AI Chatbot (5 min)

Now, let’s check if everything is working:

1. Open the chat window.
2. Type a message, such as:

   What is AI?
   

3. If the AI responds, the setup is complete!

Conclusion

By following this tutorial, you have successfully: ✅ Installed Docker and Ollama
✅ Downloaded and ran Llama 3.2
✅ Installed and configured OpenWebUI
✅ Connected OpenWebUI to Ollama
✅ Tested the chatbot

You now have a fully functional local AI chatbot running securely on your machine! 🚀

iMMAi - Set up using Local LLM Ollama, and OpenwebUI [Docker Image]

 

Here’s a step-by-step guide to setting up a local Ollama LLM with the Llama 3.2 model and using OpenWebUI as an interface. This guide is designed for  iMMbiZSofTians , so I'll keep it simple.

---

Step 1: Install Docker

We will use Docker to run OpenWebUI easily.

1. Download and install Docker Desktop from here.
2. After installation, open Docker and make sure it is running.

---

Step 2: Install Ollama

Ollama is the tool that helps run LLM models locally.

1. Download and install Ollama from https://ollama.com/download.

2. Open a terminal (Command Prompt / PowerShell / Terminal) and type:

   ollama --version
   

   If it shows a version number, that means Ollama is installed correctly.

---

Step 3: Download Llama3.2 Model

Now, let's download the Llama 3.2 model.

1. Open a terminal and run:

   ollama pull meta/llama3
   

   This will download the latest Llama 3 model (currently version 3.2).

How I made iMMAi [local LLM]

 

How I Made iMMAi: A Legal AI Assistant

Introduction

iMMAi is a powerful local AI assistant specialized in Indian Company Laws and corporate regulations. This blog will guide you through the step-by-step process of creating iMMAi using Ollama and Docker.

Step 1: Install Ollama

Ollama is the tool that allows us to run large language models locally.

1. Download and install Ollama from https://ollama.com/download.
2. Verify the installation by running:

   ollama --version
   

Step 2: Install Docker Desktop

Docker is needed to containerize and manage OpenWebUI.

1. Download and install Docker Desktop from https://www.docker.com/get-started.
2. Open Docker and make sure it is running.

Step 3: Download the Llama 3.2 Model

Now, we will pull the Llama 3.2 model, which serves as the base for iMMAi.

1. Open a terminal and run:

   ollama pull llama3.2
   

2. Verify the model is downloaded:

   ollama run llama3.2
   

Step 4: Create a Custom Modelfile for iMMAi

Now, we will customize Llama 3.2 to specialize in Indian legal and corporate regulations.

1. Create a new file named Modelfile and add the following content:

   FROM llama3.2
   SYSTEM """Your name is iMMAi! You are a very clever Legal Assistant and Chartered Accountant 
   specialized in Indian Company Laws and corporate regulations. You know everything about company registration and financial aspects. 
   You are succinct and informative. Search only for official legal and corporate regulations in India.
   Do not include foreign laws or unrelated information.
   Provide a **brief summary** in 2-3 sentences by default."""
   PARAMETER temperature 0.1
   

Step 5: Create the iMMAi Model

Now, we will create the iMMAi model using the Modelfile.

1. Open a terminal and run:

   ollama create iMMAi -f Modelfile
   

2. Check if the model is created:

   ollama list
   

   If you see iMMAi in the list, the model has been successfully created.

Step 6: Test iMMAi

Finally, let’s run and test our custom AI assistant.

1. Run iMMAi in the terminal:

   ollama run iMMAi
   

2. Ask it a legal or corporate question, such as:

   How do I register a private limited company in India?
   

3. If the response is relevant and based on Indian corporate laws, your AI assistant is ready! 🚀

Conclusion

In this blog, we successfully: ✅ Installed Ollama and Docker
✅ Downloaded and ran Llama 3.2
✅ Created a custom legal AI assistant (iMMAi)
✅ Tested iMMAi for legal and corporate queries

You now have a fully functional local AI legal assistant that can help with Indian corporate regulations. 🎯

Hour 12++ Local LLM for iMMSS AI

Let us have our Local LLM free and customize with name candy in just 5 Minutes!!!

Ollama is a free and open-source project that lets you run various open source LLMs locally on your system.

OLLAMA - Omni-Layer Learning Language Acquisition Model

• 
  
• Please download ollama and run llama2. It will take some time at first instance. Then say /bye to quit in the prompt >>>.
• Create a Modelfile with the following contents

FROM llama3.2

SYSTEM """Your name is Candy ! You are very Clever assistant who knows everything.

          You are very succinct and informative."""

PARAMETER temperature 0.1

• 
  
• #ollama create candy -f  Modelfile

Please check by

#ollama list  (my typical list is shown below. Please check candy is there

ollama list

NAME                       ID              SIZE      MODIFIED      

candy:latest               2ea6c7bb34ec    2.0 GB    58 minutes ago    

nomic-embed-text:latest    0a109f422b47    274 MB    3 hours ago      

llama3.2:latest            a80c4f17acd5    2.0 GB    3 hours ago      

mistral:latest             f974a74358d6    4.1 GB    20 hours ago      

llama3.1:latest            46e0c10c039e    4.9 GB    31 hours ago      

phi3:latest                4f2222927938    2.2 GB    38 hours ago      

phi:latest                 e2fd6321a5fe    1.6 GB    2 days ago        

 Local LLM using OLLAMA with Gradio

 main.py 

#llm-api-demo

import requests

import json

#pip install gradio

import gradio as gr

url="http://localhost:11434/api/generate"

headers = {

    "Content-Type": "application/json"

}

history=[]

import gradio as gr

#ollama create candy -f candy_modelfile.modelfile

def generate_response(prompt):

 

    history.append(prompt)

    final_prompt="\n".join(history)

    data = {

        "model": "candy",

        "prompt": final_prompt,

        "stream": False

    }

   

    response = requests.post(url, headers=headers,data=json.dumps(data))

   

    if response.status_code == 200:

        response =  response.text

        data =json.loads(response)

        actual_response=data["response"]

        return actual_response

    else:

        return "Error generating response"

   

interface = gr.Interface(

    fn=generate_response,

    inputs=gr.Textbox(lines=3,placeholder="Enter your Prompt",

label=" I am iMMSS AI Candy , How Can i Help You?"),

    outputs="text",

     title="IMMSS AI Candy",

    description="I am a helpful AI assistant that uses Ollama language

model to generate responses based on your inputs. I am also capable

of generating multiple prompts and providing a history of all your

previous prompts."

)

interface.launch()

Response :

f:/ollama/ollama-gradio.py Running on local URL:  http://127.0.0.1:7860. Go to this URL .You will get the window with input box. Type prompt as What Ministry of Corporate affairs do in India? and click SUBMIT Button. You will get response as shown below:

Eureka ! SO easy to built Customized Local LLM with Ollama & Gradio for UI.. 

Here are some examples of prompts that can be generated using the MCA in India by our Loacl iMMSS Candy LLM:

1. **Drafting a complaint letter to the MCA**: "Write a sample complaint letter to the MCA regarding a company's non-compliance with regulatory requirements."

2. **Creating a template for a company's compliance report**: "Design a template for a company's compliance report, as required by the MCA under the Companies Act, 2013."

3. **Researching on recent developments in corporate governance**: "Research and write about recent developments in corporate governance in India, highlighting the role of the MCA in promoting good governance practices."

4. **Drafting a response to an investor's query**: "Write a sample response to an investor's query regarding a company's compliance with regulatory requirements, as per the guidance provided by the MCA."

Hour 12+ Local LLM using OLLAMA

 Quick Sho{r}t   Exercises

Ex1. Building a Local LLM and extract information

ollama run llama3.1 

from langchain_community.llms import Ollama

#ollama run llama3.1

llm = Ollama(model="llama3.1")

response = llm("Tell me about Mahatma Gandhi")

print(response)

Response

A great figure in history! Mahatma Gandhi (1869-1948) was a Indian

independence activist, leader of the non-violent resistance movement

against British rule in India, and a key figure in the country's struggle

for freedom. He is widely regarded as one of the most influential

leaders of the 20th century.

**Early Life**

Gandhi was born on October 2, 1869, in Porbandar, a coastal town in the

state of Gujarat, India. His father, Karamchand Gandhi, was a local

leader and businessman, while his mother, Putlibai, was a devout Hindu

from a family of farmers. Gandhi's early life was marked by a strong

emphasis on spirituality and self-discipline.

**Education and Career**

Gandhi studied law at the University College London and later

practiced as a lawyer in India. However, he soon became disillusioned

with the British colonial system and its treatment of Indians.

In 1893, he traveled to South Africa to work as a lawyer for an Indian

firm, but his experience there exposed him to the harsh realities of

racial segregation and discrimination.      

**Non-Violent Resistance**

Gandhi's experiences in South Africa influenced his approach to politics

and led him to develop the philosophy of non-violent resistance.

He believed that peaceful protests, civil disobedience, and boycotts

could be effective tools for achieving social change without resorting

to violence.

**Indian Independence Movement**

In 1915, Gandhi returned to India and joined the Indian National

Congress (INC), a party fighting for Indian independence from British

rule. He became one of its key leaders and used his non-violent

resistance methods to challenge the British government's authority.

Some notable examples include:

1. **Salt March** (1930): A 24-day march from Ahmedabad to Dandi,

where Gandhi and thousands of followers protested against the

British Salt Act by making salt themselves.

2. **Non-Cooperation Movement** (1920-1922): Gandhi called for

Indians to boycott British goods, schools, and courts, which led

to widespread protests and arrests.

3. **Quit India Movement** (1942): A campaign that urged the British

to leave India immediately, but was met with brutal suppression.

**Philosophy and Legacy**

Gandhi's philosophy of non-violent resistance, known as "Satyagraha"

(holding onto truth), emphasized:

1. **Non-violence**: The rejection of violence in all forms, including

physical and verbal aggression.

2. **Simple living**: Emphasis on simplicity, self-sufficiency,

and a commitment to reducing one's material needs.

3. **Love and compassion**: A focus on promoting empathy, understanding,

and reconciliation.  

Gandhi's influence extends far beyond India:

1. **Civil Rights Movement** (USA): Gandhi's philosophy inspired leaders

like Martin Luther King Jr., who adopted similar non-violent resistance

methods in the American civil rights movement.

2. **International diplomacy**: Gandhi's approach has influenced global

efforts towards peaceful conflict resolution and disarmament.

**Assassination**

Tragically, Gandhi was assassinated on January 30, 1948, by a Hindu

nationalist named Nathuram Godse. The news of his death sent shockwaves

around the world, but it also cemented his legacy as a champion of peace

and non-violence.

Today, Gandhi's birthday, October 2, is celebrated as International

Day of Non-Violence, and he remains an iconic figure in history,

inspiring countless people worldwide with his message of love, compassion,

and peaceful resistance. """

Hour 12 - Wrap-Up and Q&A

 Lecture Notes: 

---

1. Wrap-Up

Overview of the Course

• Over the past 12 hours, we’ve explored Ollama LLM Basics and Hugging Face models, focusing on practical applications, implementation, and fine-tuning.
• We have covered topics like:
  • Installation of Ollama and Hugging Face libraries
  • File structure of Ollama
  • Understanding chunks, embeddings, and vector databases
  • Working with Llama models for tasks such as text generation, sentiment analysis, and question answering.
  • Fine-tuning techniques and their real-life applications, such as for PDF extraction, web scraping, and chatbot development.

Key Takeaways

1. Powerful NLP Tools: We learned how to use pre-trained models like Llama from Hugging Face for a wide range of NLP tasks.
2. Model Fine-Tuning: Fine-tuning models for domain-specific tasks can greatly improve model performance, even with relatively small datasets.
3. Practical Applications: We've seen how these models can be integrated into real-world applications like chatbots, sentiment analysis systems, and question-answering agents.
4. Metrics & Evaluation: We discussed how to measure model performance and optimize them for your tasks.

Looking Forward

• With the skills learned, you can start working on real-world projects like developing NLP-based tools for businesses, creating intelligent systems, and exploring more advanced topics in AI like multi-modal models and reinforcement learning.

---

2. Real-Life Example: Deploying an NLP Chatbot Using Hugging Face

In this real-life example, we’ll build a simple chatbot using the pre-trained Llama model from Hugging Face. The chatbot will be able to handle customer queries and provide helpful answers.

Step 1: Install Hugging Face Transformers

Ensure that you have installed the Hugging Face library.

pip install transformers

Step 2: Implementing the Chatbot

from transformers import LlamaForCausalLM, LlamaTokenizer

# Load the pre-trained Llama model and tokenizer
model_name = "meta-llama-7b-hf"
model = LlamaForCausalLM.from_pretrained(model_name)
tokenizer = LlamaTokenizer.from_pretrained(model_name)

# Function to simulate chatbot conversation
def chatbot_response(user_input):
    # Tokenize user input
    inputs = tokenizer(user_input, return_tensors="pt")
    
    # Generate the response from the model
    response = model.generate(inputs["input_ids"], max_length=100, num_return_sequences=1)

    # Decode the response to text
    response_text = tokenizer.decode(response[0], skip_special_tokens=True)
    return response_text

# Example of a conversation
user_query = "How can I reset my password?"
chatbot_answer = chatbot_response(user_query)

print(f"User: {user_query}")
print(f"Chatbot: {chatbot_answer}")

Explanation:

• Llama Model: A pre-trained Llama model is used to generate responses to user queries.
• Chatbot Simulation: The function chatbot_response() simulates a chatbot conversation. It tokenizes the user input, generates a response using the model, and decodes the result to text.
• This basic chatbot can be expanded with more sophisticated logic and additional features (e.g., storing context, handling multiple user inputs, or integrating with APIs).

---

3. Q&A: Typical Questions and Answers

Q1: What is Ollama?

• A1: Ollama is a platform for running, managing, and experimenting with large language models (LLMs). It provides an easy way to interact with LLMs, deploy models, and use them in applications.

Q2: How do I install Ollama and Hugging Face?

• A2: To install Ollama, you can run the following CLI command: ollama install. For Hugging Face, use pip install transformers datasets.

Q3: What is the role of embeddings in NLP?

• A3: Embeddings represent words or sentences as vectors in high-dimensional space. They capture semantic meaning and relationships between words, enabling tasks like similarity search, translation, and question answering.

Q4: What is a vector database and why is it important?

• A4: A vector database stores embeddings (vector representations of data) and allows fast similarity searches. It is important for tasks like document retrieval, recommendation systems, and semantic search.

Q5: How do I fine-tune a model like Llama for a specific task?

• A5: Fine-tuning involves training the pre-trained model on your task-specific dataset. You can load your dataset using the datasets library, tokenize it, and use Hugging Face’s Trainer to fine-tune the model.

Q6: What metrics should I use to evaluate my model?

• A6: Common metrics for evaluating NLP models include accuracy, F1 score, precision, recall, and perplexity. For tasks like question answering, you might also use Exact Match (EM) and F1 scores.

Q7: How can I deploy a fine-tuned model for production use?

• A7: You can deploy models using Hugging Face's Inference API, or by creating a REST API with tools like FastAPI or Flask. These tools allow your model to serve predictions over the web.

Q8: Can I use the Llama model for multi-turn conversations?

• A8: Yes, multi-turn conversations can be managed by maintaining context. You can pass previous user inputs and model responses back to the model to ensure it remembers the conversation history.

Q9: How do I preprocess data for model fine-tuning?

• A9: Preprocessing typically involves tokenizing the text, padding or truncating sequences to a fixed length, and formatting data into input-output pairs. Hugging Face's transformers and datasets libraries provide utilities for these tasks.

Q10: What are the limitations of using Llama models for certain tasks?

• A10: Llama models, like all language models, are limited by the data they were trained on. They may struggle with tasks requiring very domain-specific knowledge or tasks involving non-text data (e.g., images or sounds). Fine-tuning on relevant data can mitigate some of these limitations.

---

4. Final Thoughts

Key Concepts to Remember:

• Pre-trained models like Llama can save time and resources in NLP tasks.
• Fine-tuning enhances the ability of models to perform specific tasks.
• Hugging Face provides a rich ecosystem for working with models, datasets, and deployment tools.

Next Steps:

• Explore more Hugging Face models for various NLP tasks.
• Experiment with fine-tuning on your own custom datasets.
• Learn about advanced techniques like multi-modal models, reinforcement learning, or real-time model serving.

---

5. Thank You for Attending the Course!

• With the knowledge gained, you are now equipped to start working with language models like Llama and explore advanced AI applications.
• Keep experimenting, and don't hesitate to reach out to the community or further resources on Hugging Face to deepen your understanding!

This concludes Hour 12 on Wrap-Up and Q&A. Feel free to explore and apply what you've learned to your own projects!

Hour 11 - Practical Applications with Llama: Hugging Face Models

Lecture Notes: 

---

1. Concepts

What is Hugging Face?

Hugging Face is an open-source AI community and platform that provides powerful tools for NLP tasks. It provides access to pre-trained models like BERT, GPT, and Llama for a wide variety of tasks, such as text generation, translation, summarization, and more. It also simplifies the integration of these models with APIs for easy deployment in applications.

---

Llama Models on Hugging Face

Llama, a family of LLMs (Large Language Models) by Meta, can be easily accessed on Hugging Face. Hugging Face’s transformers library provides seamless integration of these models, allowing you to fine-tune and deploy them for various NLP tasks.

---

Practical Applications of Llama Models

Llama models, when fine-tuned for specific tasks, can be applied to real-world scenarios in industries like:

1. Text Summarization
   • Summarizing long articles, reports, research papers, and news.
2. Question Answering (QA)
   • Building intelligent chatbots or QA systems.
3. Text Generation
   • Generating creative writing, code, or completing unfinished sentences.
4. Named Entity Recognition (NER)
   • Extracting names, dates, locations, and other entities from text.
5. Translation
   • Language translation and localization.
6. Sentiment Analysis
   • Determining the sentiment (positive/negative) in customer reviews, social media posts, etc.

---

2. Key Aspects of Practical Applications

1. Pre-trained Models

   • Hugging Face provides a range of pre-trained models for various NLP tasks. These models are fine-tuned on diverse datasets and can be used immediately for real-world applications.

2. Model Fine-Tuning

   • Fine-tuning pre-trained models on task-specific datasets enhances their performance. You can adapt a general-purpose model (like Llama) to your use case.

3. APIs and Integration

   • Hugging Face offers APIs for easy model deployment and integration with applications. These can be used to make predictions in real-time via HTTP requests or integrated into chatbots, websites, etc.

4. Datasets for Training

   • Hugging Face also provides datasets for training and fine-tuning models, along with utilities to preprocess data.

5. Optimized Infrastructure

   • Hugging Face’s infrastructure (Model Hub and Inference API) allows for easy deployment on the cloud with optimized models, saving you from setting up your own infrastructure.

---

3. Implementation of Llama Models from Hugging Face

Prerequisites:

• Install necessary Python packages.

  pip install transformers datasets torch
  

---

Example 1: Using Pre-trained Llama Model for Text Generation

This example demonstrates how to use a pre-trained Llama model from Hugging Face to generate text based on a given prompt.

from transformers import LlamaForCausalLM, LlamaTokenizer

# Load the Llama model and tokenizer
model_name = "meta-llama-7b-hf"  # This is the pre-trained Llama model available on Hugging Face
model = LlamaForCausalLM.from_pretrained(model_name)
tokenizer = LlamaTokenizer.from_pretrained(model_name)

# Example prompt for text generation
prompt = "In the field of artificial intelligence,"

# Tokenize the input
inputs = tokenizer(prompt, return_tensors="pt")

# Generate text based on the input prompt
outputs = model.generate(inputs['input_ids'], max_length=100)

# Decode and print the generated text
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Explanation:

• The model is initialized from Hugging Face's meta-llama-7b-hf pre-trained model.
• The tokenizer is used to preprocess the input text (prompt).
• The model generates text based on the prompt using model.generate().
• Finally, the output text is decoded using the tokenizer.

---

Example 2: Fine-Tuning a Llama Model for Text Classification (Sentiment Analysis)

In this example, we fine-tune the Llama model for a sentiment classification task.

1. Dataset: We will use the IMDb dataset for sentiment analysis, available on Hugging Face Datasets.

2. Fine-Tuning: We will fine-tune the pre-trained model on the sentiment classification dataset.

from transformers import LlamaForSequenceClassification, Trainer, TrainingArguments
from datasets import load_dataset
from transformers import LlamaTokenizer

# Load pre-trained model and tokenizer for sequence classification
model_name = "meta-llama-7b-hf"
model = LlamaForSequenceClassification.from_pretrained(model_name, num_labels=2)
tokenizer = LlamaTokenizer.from_pretrained(model_name)

# Load the IMDb dataset
dataset = load_dataset("imdb")

# Preprocess the dataset (tokenization)
def tokenize_function(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Split the dataset into training and evaluation sets
train_dataset = tokenized_datasets["train"]
eval_dataset = tokenized_datasets["test"]

# Training arguments
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    num_train_epochs=3,
    weight_decay=0.01,
    logging_dir="./logs",
)

# Initialize Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    tokenizer=tokenizer,
)

# Train the model
trainer.train()

# Evaluate the model
results = trainer.evaluate()
print(f"Evaluation results: {results}")

Explanation:

• We load the IMDb dataset for sentiment analysis, which contains movie reviews labeled as positive or negative.
• The tokenize_function is used to preprocess the text data, making it compatible with the Llama model.
• We set up the Trainer class with the model and training parameters, and fine-tune the model on the sentiment dataset.
• Finally, the model is evaluated on a test set.

---

Example 3: Question Answering with Llama Model

In this example, we fine-tune a Llama model for a Question Answering task.

from transformers import LlamaForQuestionAnswering, LlamaTokenizer
from datasets import load_dataset

# Load pre-trained model and tokenizer for Question Answering
model_name = "meta-llama-7b-hf"
model = LlamaForQuestionAnswering.from_pretrained(model_name)
tokenizer = LlamaTokenizer.from_pretrained(model_name)

# Load the SQuAD dataset
dataset = load_dataset("squad")

# Preprocess the dataset (tokenization)
def preprocess_data(examples):
    return tokenizer(examples["question"], examples["context"], truncation=True, padding="max_length")

tokenized_data = dataset.map(preprocess_data, batched=True)

# Example Question and Context
context = "The capital of France is Paris, a city known for its culture and history."
question = "What is the capital of France?"

# Tokenize the inputs
inputs = tokenizer(question, context, return_tensors="pt")

# Get model outputs
outputs = model(**inputs)
start_scores = outputs.start_logits
end_scores = outputs.end_logits

# Get the most likely start and end positions of the answer
start_idx = torch.argmax(start_scores)
end_idx = torch.argmax(end_scores)

# Decode the answer
answer = tokenizer.decode(inputs["input_ids"][0][start_idx:end_idx+1], skip_special_tokens=True)
print(f"Answer: {answer}")

Explanation:

• This code uses the SQuAD dataset (Stanford Question Answering Dataset) for fine-tuning the model for a question answering task.
• The model is fine-tuned using the LlamaForQuestionAnswering class from Hugging Face's Transformers library.
• After fine-tuning, we provide a context and a question, and the model predicts the answer by identifying the most likely span of text in the context.

---

4. Real-Life Example: Text Generation for Customer Support Chatbot

In this example, we use the pre-trained Llama model for generating responses in a customer support chatbot. The chatbot takes customer queries and generates text responses.

1. Objective: Use a pre-trained Llama model to simulate a customer support agent.
2. Use Case: Handle queries like "How do I reset my password?" or "Where can I find my order history?"

from transformers import LlamaForCausalLM, LlamaTokenizer

# Load pre-trained model and tokenizer
model_name = "meta-llama-7b-hf"
model = LlamaForCausalLM.from_pretrained(model_name)
tokenizer = LlamaTokenizer.from_pretrained(model_name)

# Simulate customer query
customer_query = "How do I reset my password?"

# Tokenize input
inputs = tokenizer(customer_query, return_tensors="pt")

# Generate response
response = model.generate(inputs["input_ids"], max_length=50)

# Decode and print the response
generated_response = tokenizer.decode(response[0], skip_special_tokens=True)
print(f"Customer Support Response: {generated_response}")

Explanation:

• A customer query is tokenized and passed to the pre-trained Llama model.
• The model generates a response based on the input query.
• This setup can be scaled to create intelligent chatbots that can handle a wide variety of queries.

---

5. Summary

• Hugging Face's Transformers provides an easy way to deploy and fine-tune Llama models for practical applications.
• Llama models can be utilized for tasks such as text generation, sentiment analysis, question answering, and chatbot development.
• Fine-tuning these models on task-specific datasets allows them to adapt and excel in real-world applications.
• Hugging Face makes model deployment and API integration simple, enabling businesses and developers to leverage powerful NLP models easily.

---

6. Homework/Practice

1. Fine-tune a Llama model on a custom dataset for a real-world application (e.g., email classification, FAQ answering).
2. Build a small chatbot using Llama for customer support, implementing features such as handling product-related questions.
3. Explore Hugging Face’s Model Hub to experiment with different models and tasks.
4. Investigate how to deploy a fine-tuned Llama model using Hugging Face’s Inference API.

---

This concludes Hour 11 on Practical Applications with Llama and Hugging Face Models.