diff --git a/legalysis/README.md b/legalysis/README.md
new file mode 100644
index 00000000..40cca859
--- /dev/null
+++ b/legalysis/README.md
@@ -0,0 +1,43 @@
+#### Team Name - Legalysis
+#### Problem Statement - AI-Enhanced Legal Practice Platform
+#### Team Leader Email - tummuri.hari1@wipro.com
+
+#### A Brief of the Prototype:
+      To proficiently address these challenges, we’ve utilized diverse capabilities within large language models (LLMs). Our solution encompasses tasks such as summarizing legal documents, researching past cases, recalling IPC sections, and developing litigation strategies. Leveraging Intel’s robust hardware and the powerful Intel OneAPI AI analytics toolkit, we’ve fine-tuned and optimized LLMs on extensive datasets, culminating in a comprehensive and efficient solution. Alongside fine-tuning, we incorporated Retrieval-Augmented Generation to enhance our approach for the mentioned use cases.
+
+#### Prototype Description:
+  To develop this solution, we have gathered data from diverse sources, which consists of legal case judgments, legal proceedings, and the Indian penal code. These     datasets underwent pre-processing, and along with large language model capabilities we utilized various methodologies to devise effective solutions.
+
+  For summarizing the legal documents, we have trained LLAMA2 7B model with legal document summarization dataset resulting in performance improvement of LLM for        legal case summarization. And we have done finetuning using Intel AI analytics toolkit.
+
+  For Legal research, we’ve integrated LLM with RAG (Retrieval Augmented Generation) to access details about specific cases of interest to users. This integration      facilitates the provision of valuable insights for their case proceedings.
+
+  For IPC related queries, we have fine-tuned a T5 large model using an IPC dataset and used Intel developer cloud for finetuning. This enables the model to identify   the specific IPC section applicable to a given scenario.
+
+  ![Architecture](https://github.com/hari-tummuri/oneAPI-GenAI-Hackathon-2023/assets/104126503/4919b2c9-9ea3-43a0-8bcb-c1bfd2dd74b4)
+
+![Process Flow Diagram](https://github.com/hari-tummuri/oneAPI-GenAI-Hackathon-2023/assets/104126503/7678e04b-7c72-42ff-a353-3656a448d636)
+
+  
+
+  
+  
+  
+#### Step-by-Step Code Execution Instructions:
+  
+1.  Login to IDC(Intel developer cloud)
+2.  Install node,express,ngrok
+3.  import main.js file
+4.  import llama.py
+5.  login to ngrok account
+6.  run the node files
+7.  Ngrok will give a url to make a API call to the node.js file for tunneling
+8.  the user has to update the ngrok url in app.py file.
+9.  the user has to install all the requirements in requirements.txt file
+10. the user has to run the app.py streamlit file
+    
+
+   
+
+#### Future scope:
+      To enhance the solution’s effectiveness, we plan to incorporate litigation strategy development in the near future by fine-tuning an LLM and integrating Retrieval-Augmented Generation.
diff --git a/legalysis/application/idc/llama.py b/legalysis/application/idc/llama.py
new file mode 100644
index 00000000..d8966763
--- /dev/null
+++ b/legalysis/application/idc/llama.py
@@ -0,0 +1,18 @@
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import sys
+ 
+description = sys.argv[1]
+tokenizer = AutoTokenizer.from_pretrained("Bhuvanesh-Ch/summarizationFineTuned")
+model = AutoModelForCausalLM.from_pretrained("Bhuvanesh-Ch/summarizationFineTuned")
+prompt = description.replace("#", " ")
+inputs = tokenizer(prompt, return_tensors='pt')
+output = tokenizer.decode(
+    model.generate(
+        inputs["input_ids"],
+        max_new_tokens=1000,
+    )[0],
+    skip_special_tokens=True
+)
+ 
+print(output)
+ 
\ No newline at end of file
diff --git a/legalysis/application/idc/main.js b/legalysis/application/idc/main.js
new file mode 100644
index 00000000..7719f652
--- /dev/null
+++ b/legalysis/application/idc/main.js
@@ -0,0 +1,43 @@
+const express = require('express');
+const bodyParser = require('body-parser');
+const { promisify } = require('util');
+const { execFile } = require('child_process');
+ 
+const app = express();
+const port = 3000;
+const cors = require('cors');
+app.use(cors());
+const script1Path = 'llama.py';
+const script2Path = "IPC.py";
+ 
+const execFilePromise = promisify(execFile);
+ 
+app.use(bodyParser.json());
+app.use(bodyParser.urlencoded({ extended: false }));
+ 
+app.get('/', (req, res) => {
+    res.send('Initiating the system');
+});
+ 
+app.post('/', async (req, res) => {
+    let data = req.body.data;
+    let description = req.body.description;
+  description = description.replace(/ /g, "#");
+    if (data === "Summarization") {
+        try {
+            const { stdout } = await execFilePromise('/home/ubuntu/jupyter_env/bin/python3', ['llama.py', description]);
+            console.log('Python process completed successfully');
+            res.send({ outputData: stdout });
+        } catch (error) {
+            console.error('Error executing Python process:', error.message);
+            res.status(500).send({ error: error.message });
+        }
+    }
+ 
+ 
+});
+ 
+app.listen(port, () => {
+    console.log(`Server is listening on http://localhost:${port}`);
+});
+ 
\ No newline at end of file
diff --git a/legalysis/application/idc/red.pd b/legalysis/application/idc/red.pd
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/legalysis/application/idc/red.pd
@@ -0,0 +1 @@
+
diff --git a/legalysis/application/locul_run/app.py b/legalysis/application/locul_run/app.py
new file mode 100644
index 00000000..4fa90fb0
--- /dev/null
+++ b/legalysis/application/locul_run/app.py
@@ -0,0 +1,308 @@
+import streamlit as st
+from streamlit_option_menu import option_menu
+import json
+import requests  # pip install requests
+#import streamlit as st  # pip install streamlit
+from streamlit_lottie import st_lottie  # pip install streamlit-lottie
+import time  
+from datetime import date
+
+import requests
+
+# GitHub: https://github.com/andfanilo/streamlit-lottie
+# Lottie Files: https://lottiefiles.com/
+
+def load_lottiefile(filepath: str):
+    with open(filepath, "r") as f:
+        return json.load(f)
+
+
+def load_lottieurl(url: str):
+    r = requests.get(url)
+    if r.status_code != 200:
+        return None
+    return r.json()
+    
+
+lottie_coding = load_lottiefile("lottiefile.json")  # replace link to local lottie file
+lottie_hello = load_lottieurl("https://assets9.lottiefiles.com/packages/lf20_M9p23l.json")
+
+
+st.set_page_config(layout="wide")
+
+st.markdown("""<img src='https://logo6303.s3.amazonaws.com/logo6.png' width=350 height= 80  >""", True)
+
+col3,col4 = st.columns([7,3])
+
+with col3:
+    st.markdown("""<br><p style="font-family:Verdana;font-size: 15px;float:left;padding:5rem 5rem 0 0">Revolutionize your legal approach with AI-powered Document summarization, Legal case analasys and developing Litigation strategies. Streamline processes and gain crucial insights for optimized outcomes.</p>""",True)
+
+with col4:
+    st_lottie(
+    lottie_coding,
+    speed=1,
+    reverse=False,
+    loop=True,
+    quality="low", # medium ; high
+    #renderer="svg", # canvas
+    height=250,
+    width=250,
+    key=None,
+)
+
+    
+
+# st.title("Legalysis")
+# st.caption("Revolutionize your legal approach with AI-powered litigation strategies and document summarization. Streamline processes and gain crucial insights for optimized outcomes.")
+st.markdown(
+        """
+        <style>
+            .title {
+                background-image: url('C:/Users/Kodee/Desktop/webapp/images/law.jpg');
+                background-size: cover;
+                color: #ffffff;
+            }
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+selected=option_menu(
+        menu_title=None, #mandatory required
+        options=["Summarize Document","Legal Research","IPC","Litigation Strategy"], 
+        icons=["book","house","bezier","upc-scan"],#optional
+        menu_icon=["cast"],
+        default_index=0,
+        orientation="horizontal",
+                    styles={
+                "container": {"padding": "0!important", "background-color": "#fafafa"},
+                "icon": {"color": "orange", "font-size": "25px"},
+                "nav-link": {
+                    "font-size": "20px",
+                    "text-align": "center",
+                    "margin": "0px",
+                    "--hover-color": "#eee",
+                },
+                "nav-link-selected": {"background-color": "green"},
+            },
+    )
+
+  
+
+import PyPDF2
+
+
+def set_background_color(color):
+    """
+    Set the background color of the entire app.
+
+    Parameters:
+    - color (str): The background color in CSS format (e.g., 'lightblue').
+    """
+    page_bg_color = f"""
+        <style>
+            body {{
+                background-color: {color};
+            }}
+        </style>
+    """
+    st.markdown(page_bg_color, unsafe_allow_html=True)
+
+def read_pdf(file):
+    pdf_reader = PyPDF2.PdfReader(file)
+    text = ""
+    for page_num in pdf_reader.pages:
+        text += page_num.extract_text()
+    return text
+
+
+
+
+def main():
+    # st.header(":green[Upload your file]")
+    st.markdown("""<h3 style="color:#3F000F;font-family:Verdana">Upload your file</h3>""",True)
+    st.markdown("""  
+    <style>  
+    .stButton>button {  
+        background-color: #4CAF50;  
+        color: white;
+        margin-top: 20px;  
+    }, 
+    </style>  
+    """, unsafe_allow_html=True) 
+
+    #create columns
+    col1, col2 = st.columns(2)
+
+    # File upload widgets
+    uploaded_file = col1.file_uploader("Upload a file", type=["pdf", "txt"],label_visibility="collapsed")
+    
+
+    if uploaded_file is not None:
+        st.success("File successfully uploaded!")
+
+   
+
+    if col2.button('Generate summary'):
+        if uploaded_file is not None:
+           
+            if uploaded_file.type == "application/pdf":
+                st.write("### PDF Content")
+                pdf_text =  read_pdf(uploaded_file)
+                st.write(pdf_text)
+
+            elif uploaded_file.type == "text/plain":
+                text_content = uploaded_file.getvalue().decode("utf-8")
+                API_URL = "Input the ngrok url from Intel Developer Cloud"
+                def query(payload):
+                    response = requests.post(API_URL, json=payload)
+                    return response.json()
+                output = query({ "data":"Summarization"," description": text_content})
+                output1 = output[0]['generated_text']
+                result = output1.replace(text_content," ")
+                
+                
+                html_code1 = """<div style="background-color:#ffffff;padding:50px;border-radius: 10px">  
+                        <p style="text-align:center;font-family:Verdana">{}</p></div> """.format(result)
+            
+                # Display a message in white color  
+                st.markdown('<h4 style="color:#3F000F;font-family:Verdana">Summarizing content....</h4>', unsafe_allow_html=True)  
+
+                # st.write("### Summarized Content")
+                st.markdown("""<h4 style="color:#3F000F;font-family:Verdana">Summarized Content</h4>""",True)
+                st.markdown(html_code1, unsafe_allow_html=True)
+        
+        else:
+            st.write("No file uploaded") 
+
+def rag(question):
+    from langchain.text_splitter import RecursiveCharacterTextSplitter
+    from langchain.chains import RetrievalQA
+    from langchain.document_loaders import TextLoader
+    from langchain.document_loaders import PyPDFLoader
+    from langchain.document_loaders import DirectoryLoader
+    from langchain.embeddings import HuggingFaceEmbeddings
+    from langchain.vectorstores import Chroma
+    from langchain.chains.question_answering import load_qa_chain
+    from langchain import HuggingFaceHub
+
+    # Load and process the pdf files
+    loader = DirectoryLoader('./cases', glob="./*.pdf", loader_cls=PyPDFLoader)
+    documents = loader.load()
+
+    #splitting the text into
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(documents)
+
+    # Embeddings
+    embeddings = HuggingFaceEmbeddings()
+    db = Chroma.from_documents(texts, embeddings)
+    llm=HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature":0.02, "max_length":512},huggingfacehub_api_token='token')
+    chain = load_qa_chain(llm, chain_type="stuff")
+    search_type="mmr"
+    docs = db.search(question,search_type)
+
+    return chain.run(input_documents=docs, question=question)
+
+def text_input(selected):
+    # st.header("Legal Assistant")
+    st.markdown("""<h3 style="color:#3F000F;font-family:Verdana">{} Assistant</h3>""".format(selected),True)
+
+    # #text input widget
+    # question = st.text_area("Ask your question", placeholder='Message your assistant', height=2,label_visibility="collapsed" )
+
+    if selected == "Legal Research":
+        question = st.text_area("Ask your question", placeholder='Message your assistant', height=2,label_visibility="collapsed" )
+        if question != "":
+            
+            # Display a message in white color  
+            st.markdown('<h3 style="color:#3F000F;font-family:Verdana">Generating content....</h3>', unsafe_allow_html=True) 
+        
+            result = rag(question)
+            html_code2 = """<div style="background-color:#ffffff;padding:50px;border-radius: 10px">  
+                        <p style="text-align:center;font-family:Verdana">{}</p></div> """.format(result)
+            st.markdown("""<h3 style="color:#3F000F;font-family:Verdana">Generated content</h3>""",True)
+            st.markdown(html_code2, unsafe_allow_html=True)
+            question = ""
+    if selected == "IPC":
+        question1 = st.text_area("Ask your question", placeholder='Message your assistant', height=2,label_visibility="collapsed" )
+        if question1 != "":
+            API_URL = "https://api-inference.huggingface.co/models/Bhuvanesh-Ch/Tout"
+            headers = "huggingface key"
+            def query(payload):
+                response = requests.post(API_URL, headers=headers, json=payload)
+                return response.json()
+            
+            output = query({ "inputs": "What does Section 10000000 from XVII about?s", })
+            res = output[0]['generated_text']
+            # Display a message in white color  
+            st.markdown('<h3 style="color:#3F000F;font-family:Verdana">Searching....</h3>', unsafe_allow_html=True)
+            
+        
+            # st.write("### Summarized Content")
+            html_code3 = """<div style="background-color:#ffffff;padding:50px;border-radius: 10px">  
+                        <p style="text-align:center;font-family:Verdana">{}</p></div> """.format(res)
+            
+            st.markdown("""<h3 style="color:#3F000F;font-family:Verdana">Search result</h3>""",True)
+            st.markdown(html_code3, unsafe_allow_html=True) 
+    if selected == "Litigation Strategy":
+        question1 = st.text_area("Ask your question", placeholder='Message your assistant', height=2,label_visibility="collapsed" )
+        if question1 != "":
+            
+            # Display a message in white color  
+            st.markdown('<h3 style="color:#3F000F;font-family:Verdana">Generating litigation strategy....</h3>', unsafe_allow_html=True)
+            API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
+            headers = {"Authorization": "Bearer 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"}
+            def query(payload):
+                response = requests.post(API_URL, headers=headers, json=payload)
+                return response.json()
+            inp = "Give me a decent litigation strategy with key arguments for the following in 50 words and don't include the prompt I sent."+" question1"+" Your strategy should aim to protect your client's interests, minimize potential damages, and achieve a favorable outcome in the litigation. "
+            output = query({ "inputs": inp, })
+            output1 = output[0]['generated_text']
+            result = output1.replace(inp," ")
+            
+            # st.write("### Summarized Content")
+            st.markdown("""<h3 style="color:#3F000F;font-family:Verdana">Litigation strategy</h3>""",True)
+            html_code4 =  """<div style="background-color:#ffffff;padding:50px;border-radius: 10px">  
+            <p style="text-align:center;">{}</p>  
+            </div> """.format(result)
+            st.markdown(html_code4, unsafe_allow_html=True) 
+
+
+if selected=="Litigation Strategy":
+    # st.header("In Progress...")
+    # st.markdown("""<h2 style="color:#ffffff">In Progress...</h2>""",True)
+    text_input(selected)
+if selected=="Summarize Document":
+    #st.title(f"Please upload your file for summarization")
+    main()
+if selected == "Legal Research":
+    text_input(selected)
+if selected == "IPC":
+    text_input(selected)
+
+# # st.set_page_config(page_title="My first project",layout="wide", page_icon=":tada:")
+# # # ---- HEADER SECTION ----
+# # with st.container():
+# #     st.subheader("Hi, I am Kodees :wave:")
+# #     st.title("Streamlit option menu")
+# #     st.write(
+# #         "Instead of looking at my page why can't you just focus on your work."
+# #     )
+# #     st.write("[Learn More >](https://pythonandvba.com)")
+
+
+# Create a lot of empty space before the footer
+for _ in range(4):  
+    st.write("\n") 
+
+# Get the current year  
+current_year = date.today().year  
+
+# Display the copyright notice  
+# st.markdown('&#169; Copyright 2023 Legalysis, Inc. All rights reserved.', unsafe_allow_html=True)  
+st.markdown("""<p style="color:#595959;text-align: center;">&#169; Copyright 2023 Legalysis, Inc. All rights reserved.</p>""",True)
+
+
+
+
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\ No newline at end of file
diff --git a/legalysis/application/locul_run/rag.py b/legalysis/application/locul_run/rag.py
new file mode 100644
index 00000000..f8d27b11
--- /dev/null
+++ b/legalysis/application/locul_run/rag.py
@@ -0,0 +1,35 @@
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+# from langchain.llms import OpenAI
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import TextLoader
+from langchain.document_loaders import PyPDFLoader
+from langchain.document_loaders import DirectoryLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chains.question_answering import load_qa_chain
+from langchain import HuggingFaceHub
+
+# from InstructorEmbedding import INSTRUCTOR
+
+
+# Load and process the pdf files
+loader = DirectoryLoader('./cases', glob="./*.pdf", loader_cls=PyPDFLoader)
+documents = loader.load()
+
+#splitting the text into
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+texts = text_splitter.split_documents(documents)
+
+# Embeddings
+embeddings = HuggingFaceEmbeddings()
+
+db = Chroma.from_documents(texts, embeddings)
+
+llm=HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature":0.02, "max_length":512},huggingfacehub_api_token='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')
+
+chain = load_qa_chain(llm, chain_type="stuff")
+
+query = "who is the judge for  the case Shaikh Maqsood v State of Maharashtra ? give only his/her name"
+search_type="mmr"
+docs = db.search(query,search_type)
+print(chain.run(input_documents=docs, question=query))
diff --git a/legalysis/application/locul_run/sample.py b/legalysis/application/locul_run/sample.py
new file mode 100644
index 00000000..d5c94019
--- /dev/null
+++ b/legalysis/application/locul_run/sample.py
@@ -0,0 +1,29 @@
+import streamlit as st
+
+
+def set_background_color(color):
+    """
+    Set the background color of the entire app.
+
+    Parameters:
+    - color (str): The background color in CSS format (e.g., 'lightblue').
+    """
+    page_bg_color = f"""
+        <style>
+            body {{
+                background-color: {color};
+            }}
+        </style>
+    """
+    st.markdown(page_bg_color, unsafe_allow_html=True)
+
+def main():
+    # Set background color to lightblue
+    set_background_color('green')
+
+    # Your Streamlit app content
+    st.title("My Streamlit App")
+    st.write("This is the main content of your app.")
+
+if __name__ == "__main__":
+    main()
diff --git a/legalysis/application/red.pd b/legalysis/application/red.pd
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/legalysis/application/red.pd
@@ -0,0 +1 @@
+
diff --git a/legalysis/notebooks/IPC_FineTuningUsingIDC.ipynb b/legalysis/notebooks/IPC_FineTuningUsingIDC.ipynb
new file mode 100644
index 00000000..9613a08e
--- /dev/null
+++ b/legalysis/notebooks/IPC_FineTuningUsingIDC.ipynb
@@ -0,0 +1,505 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# importing libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73e4a60f-fbfc-40e6-af07-3eef6e0ec172",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import intel_extension_for_pytorch as ipex\n",
+    "print(torch.backends.mkl.is_available())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a8ba182-b638-4554-9b24-6b7a0e423e7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from distributed import Client\n",
+    "client = Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2823cb75-0dfa-4b42-ae33-292af17e2c88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import modin.pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38610962-f245-432a-8644-16b7fbe71dbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_excel(\"IPC_Dataset.xlsx\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6139b0e4-a982-4114-8184-ae1878563a35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset, Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53c5e167-ebfc-46d1-bdf2-223dd21c0f7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dict = df.to_dict('list')  # convert DataFrame to dictionary of lists\n",
+    "dataset = Dataset.from_dict(data_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data processing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c670c16-9738-4434-8e13-023d5fbf04ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = dataset.train_test_split(test_size=0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffa00726-80d0-469b-a352-a2b5922dc04c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer\n",
+    "\n",
+    "checkpoint = \"google/flan-t5-large\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0271cffd-f088-43dd-9e91-4ef770cb0e8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def format_instruction(chapter: str, section: str):\n",
+    "\treturn f\"\"\"### Instruction:\n",
+    "What does {section.strip()}  about?\n",
+    "\"\"\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e2e4364-d0f1-4971-95c3-ac93efd5c8e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_instruction_dataset(data_point):\n",
+    "\n",
+    "    return {\n",
+    "        \"chapter\": data_point[\"Chapter\"],\n",
+    "        \"Section\": data_point[\"Section\"],\n",
+    "        # \"Title\": data_point[\"Title\"],\n",
+    "        # \"Description\": data_point[\"Description\"],\n",
+    "        \"text\": format_instruction(data_point[\"Chapter\"],data_point[\"Section\"])\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6d897b1-a7bf-4fa8-a7a5-51d3be254803",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_dataset(data: Dataset):\n",
+    "    return (\n",
+    "        data.shuffle(seed=42)\n",
+    "        .map(generate_instruction_dataset)\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4ad9f65-db8b-4a35-9d83-3fdba5232da9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[\"train\"] = process_dataset(ds[\"train\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1daf4c3b-2fc1-4aa7-8915-3dee85b2f15e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[\"train\"][\"text\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a005331-b8be-4bc6-b3b0-e3d72d953845",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def format_instruction1(title: str, Description : str):\n",
+    "\treturn f\"\"\"\n",
+    "# {title.strip()} which describes {Description.strip()}\n",
+    "# \"\"\".strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f41003eb-6c66-4df2-8348-9bcd5fc57590",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_instruction_dataset1(data_point):\n",
+    "\n",
+    "    return {\n",
+    "        \"chapter\": data_point[\"Chapter\"],\n",
+    "        \"Section\": data_point[\"Section\"],\n",
+    "        \"Title\": data_point[\"Title\"],\n",
+    "        \"Description\": data_point[\"Description\"],\n",
+    "        \"text\": format_instruction1(data_point[\"Title\"],data_point[\"Description\"])\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5b3d210-2ae1-4ba9-bfec-738816acba00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_dataset1(data: Dataset):\n",
+    "    return (\n",
+    "        data.shuffle(seed=42)\n",
+    "        .map(generate_instruction_dataset1)\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70904e8e-7bdc-477b-adb9-896ecf7aa6e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[\"out\"] = process_dataset1(ds[\"train\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0bdad5a3-82be-4c36-95fc-b696b01a964d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[\"out\"][\"text\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04f9c637-7484-49cb-a0ce-d9b11e840649",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[\"title\"] = ds[\"train\"][\"text\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "057a159e-b355-4141-ae64-482eb9bf40dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[\"res\"] = ds[\"out\"][\"text\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c94c6f5-7419-4fe2-b2b1-cc49ad8b50e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfs= pd.DataFrame()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8f4e1fa-e08e-4519-9944-ea307cd341ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfs[\"title\"] = ds[\"title\"] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4767b8b-e5f2-4ef6-bc74-9eb216c70deb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfs[\"response\"] = ds[\"res\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cbd62325-a6ef-4add-ab44-8775e4d79bfe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfs[\"title\"][3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5b12774-0146-41f2-ba3e-d74eeb97806f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfs[\"response\"][3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8e19560-c09c-40d3-896c-e4acc75c3896",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fin = Dataset.from_pandas(dfs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39cfb4fd-69a8-4e8a-a1bb-c62bffd9eb67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "finalDs= fin.train_test_split(test_size=0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9a316d4-0d1e-4585-855b-94c7de590eed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def preprocess_function(examples):\n",
+    "    inputs = [doc for doc in examples[\"title\"]]\n",
+    "    model_inputs = tokenizer(inputs, max_length=1024, truncation=True)\n",
+    "\n",
+    "    labels = tokenizer(text_target=examples[\"response\"], max_length=500, truncation=True)\n",
+    "\n",
+    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
+    "    return model_inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c84f2496-c688-4741-a7eb-ee1be1d9928c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenized_ds = finalDs.map(preprocess_function, batched=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2177f0e-f4ab-4ec2-b4b4-3663fff9289a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import DataCollatorForSeq2Seq\n",
+    "\n",
+    "data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4673126c-89d2-456d-a2ed-07e9e3d0f0c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import evaluate\n",
+    "\n",
+    "rouge = evaluate.load(\"rouge\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "906e1a7c-c44d-4f93-91a8-05d6877b0771",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "def compute_metrics(eval_pred):\n",
+    "    predictions, labels = eval_pred\n",
+    "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
+    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
+    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
+    "\n",
+    "    result = rouge.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
+    "\n",
+    "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]\n",
+    "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
+    "\n",
+    "    return {k: round(v, 4) for k, v in result.items()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# finetuning LLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b82afd97-b05c-4ae8-b731-2a847d503bc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
+    "\n",
+    "model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ce5c181-a6ba-4229-9a81-48d5eb11e9c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "training_args = Seq2SeqTrainingArguments(\n",
+    "    output_dir=\"finalIPC\",\n",
+    "    evaluation_strategy=\"epoch\",\n",
+    "    learning_rate=1e-4,\n",
+    "    per_device_train_batch_size=16,\n",
+    "    per_device_eval_batch_size=16,\n",
+    "    weight_decay=0.01,\n",
+    "    save_total_limit=1,\n",
+    "    num_train_epochs=5,\n",
+    "    predict_with_generate=True,\n",
+    "    push_to_hub=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d5e5b7b-0913-4614-b88c-f55da8d767c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer = Seq2SeqTrainer(\n",
+    "    model=model,\n",
+    "    args=training_args,\n",
+    "    train_dataset=tokenized_ds[\"train\"],\n",
+    "    eval_dataset=tokenized_ds[\"test\"],\n",
+    "    tokenizer=tokenizer,\n",
+    "    data_collator=data_collator,\n",
+    "    compute_metrics=compute_metrics,\n",
+    "    \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22a6638c-8ad9-4ae2-9728-b3414acd090e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1acafe9e-69b5-45dc-99b3-a1d32d6e3c0c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/legalysis/notebooks/SummarizingLLM.ipynb b/legalysis/notebooks/SummarizingLLM.ipynb
new file mode 100644
index 00000000..1f139c0f
--- /dev/null
+++ b/legalysis/notebooks/SummarizingLLM.ipynb
@@ -0,0 +1,504 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "590ce047",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11269210",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from distributed import Client\n",
+    "client = Client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9986f377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import intel_extension_for_pytorch as ipex\n",
+    "import time\n",
+    "import evaluate\n",
+    "import modin.pandas as pd\n",
+    "import numpy as np\n",
+    "from datasets import Dataset, load_dataset\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cfb7ccc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# loading training dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d97f78e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "excel_path = 'train_dataset.xlsx'\n",
+    "df = pd.read_excel(excel_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a8f2a46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "226716c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# processing data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cbfefcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['summary'] =  df['summary'].astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf567939",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['text'] =  df['text'].astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44e4662d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dict = df.to_dict('list')  # convert DataFrame to dictionary of lists\n",
+    "dataset = Dataset.from_dict(data_dict)  # create Dataset object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97e40723",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = dataset.train_test_split(test_size=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0882b88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[\"train\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a54a63df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def format_instruction(text: str, summary: str):\n",
+    "\treturn f\"\"\"### Instruction:\n",
+    "Summarize the following case details\n",
+    "### Input:\n",
+    "{text.strip()}\n",
+    "\n",
+    "### Summary:\n",
+    "{summary.strip()}\n",
+    "\"\"\".strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afad7a39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_instruction_dataset(data_point):\n",
+    "\n",
+    "    return {\n",
+    "        \"text\": data_point[\"text\"],\n",
+    "        \"summary\": data_point[\"summary\"],\n",
+    "        \"text\": format_instruction(data_point[\"text\"],data_point[\"summary\"])\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8be89709",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_dataset(data: Dataset):\n",
+    "    return (\n",
+    "        data.shuffle(seed=42)\n",
+    "        .map(generate_instruction_dataset)\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69541801",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[\"train\"] = process_dataset(ds[\"train\"])\n",
+    "ds[\"test\"] = process_dataset(ds[\"test\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16faa2c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[\"train\"][\"text\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04feb929",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# loading the LLM models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4aa18d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
+    "\n",
+    "model_id =  \"meta-llama/Llama-2-7b-hf\"\n",
+    "bnb_config = BitsAndBytesConfig(\n",
+    "    load_in_4bit=True,\n",
+    "    bnb_4bit_use_double_quant=True,\n",
+    "    bnb_4bit_quant_type=\"nf4\",\n",
+    "    bnb_4bit_compute_dtype=torch.bfloat16\n",
+    ")\n",
+    "\n",
+    "model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+    "tokenizer.pad_token = tokenizer.eos_token\n",
+    "tokenizer.padding_side = \"right\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32931fa8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def print_trainable_parameters(model):\n",
+    "    \"\"\"\n",
+    "    Prints the number of trainable parameters in the model.\n",
+    "    \"\"\"\n",
+    "    trainable_params = 0\n",
+    "    all_param = 0\n",
+    "    for _, param in model.named_parameters():\n",
+    "\n",
+    "        all_param += param.numel()\n",
+    "        if param.requires_grad:\n",
+    "            trainable_params += param.numel()\n",
+    "    print(\n",
+    "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ee9cb82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from peft import prepare_model_for_kbit_training\n",
+    "\n",
+    "model.gradient_checkpointing_enable()\n",
+    "model = prepare_model_for_kbit_training(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b957b24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ec4a990",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Trainable parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00d26f3c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from peft import LoraConfig, get_peft_model\n",
+    "\n",
+    "lora_config = LoraConfig(\n",
+    "    r=16,\n",
+    "    lora_alpha=64,\n",
+    "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\"], #specific to Llama models.\n",
+    "    lora_dropout=0.1,\n",
+    "    bias=\"none\",\n",
+    "    task_type=\"CAUSAL_LM\"\n",
+    ")\n",
+    "\n",
+    "model = get_peft_model(model, lora_config)\n",
+    "print_trainable_parameters(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75a33549",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Output_Dir = \"Output_Dir\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0580cd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fine tuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03778e29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import TrainingArguments\n",
+    "\n",
+    "training_arguments = TrainingArguments(\n",
+    "    per_device_train_batch_size=4,\n",
+    "    gradient_accumulation_steps=4,\n",
+    "    optim=\"paged_adamw_32bit\",\n",
+    "    logging_steps=1,\n",
+    "    learning_rate=1e-4,\n",
+    "    fp16=True,\n",
+    "    max_grad_norm=0.3,\n",
+    "    num_train_epochs=4,\n",
+    "    evaluation_strategy=\"steps\",\n",
+    "    eval_steps=0.2,\n",
+    "    warmup_ratio=0.05,\n",
+    "    save_strategy=\"epoch\",\n",
+    "    group_by_length=True,\n",
+    "    output_dir=OUTPUT_DIR,\n",
+    "    report_to=\"tensorboard\",\n",
+    "    save_safetensors=True,\n",
+    "    lr_scheduler_type=\"cosine\",\n",
+    "    seed=42,\n",
+    ")\n",
+    "model.config.use_cache = False  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bb43756",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from trl import SFTTrainer\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    train_dataset=ds[\"train\"],\n",
+    "    eval_dataset=ds[\"test\"],\n",
+    "    peft_config=lora_config,\n",
+    "    dataset_text_field=\"text\",\n",
+    "    max_seq_length=2250,\n",
+    "    tokenizer=tokenizer,\n",
+    "    args=training_arguments,\n",
+    ")\n",
+    "\n",
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e110726",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peft_model_path=\"./fineTunedModel\"\n",
+    "trainer.model.save_pretrained(peft_model_path)\n",
+    "tokenizer.save_pretrained(peft_model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4241a653",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from peft import AutoPeftModelForCausalLM\n",
+    "from transformers import AutoTokenizer\n",
+    "\n",
+    "peft_model_dir = \"./fineTunedModel\"\n",
+    "\n",
+    "trained_model = AutoPeftModelForCausalLM.from_pretrained(\n",
+    "    peft_model_dir,\n",
+    "    low_cpu_mem_usage=True,\n",
+    "    torch_dtype=torch.float16,\n",
+    "    load_in_4bit=True,\n",
+    ")\n",
+    "tokenizer = AutoTokenizer.from_pretrained(peft_model_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9545612e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_ids = tokenizer(prompt, return_tensors='pt',truncation=True).input_ids.cuda()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d0af622",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "outputs = trained_model.generate(input_ids=input_ids, max_new_tokens=100, )\n",
+    "output= tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34f59cfb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cbb7b6a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trained_model = AutoPeftModelForCausalLM.from_pretrained(\n",
+    "    peft_model_dir,\n",
+    "    low_cpu_mem_usage=True,\n",
+    "    torch_dtype=torch.float16,\n",
+    ")\n",
+    "\n",
+    "fineTunedModel = trained_model.merge_and_unload()\n",
+    "\n",
+    "merged_model.save_pretrained(\"fineTunedModel\",safe_serialization=True)\n",
+    "tokenizer.save_pretrained(\"fineTunedModel\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebb2e735",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/legalysis/requirements.txt b/legalysis/requirements.txt
new file mode 100644
index 00000000..1a8354d2
--- /dev/null
+++ b/legalysis/requirements.txt
@@ -0,0 +1,11 @@
+python
+intel_extension_for_pytorch as ipex
+modin
+transformers
+torch
+streamlit
+express
+datasets
+accelerate
+bitsandbytes
+chromadb