[     {         "topic": "advanced economics",         "question": "how does the lucas critique challenge traditional macroeconomic policy analysis?",         "answer": "the lucas critique argues that traditional macroeconomic models' parameters are not policy-invariant because economic agents adjust their behavior based on expected policy changes, making historical relationships unreliable for policy evaluation."     },     {         "topic": "quantum physics",         "question": "explain quantum entanglement and its implications for quantum computing.",         "answer": "quantum entanglement is a physical phenomenon where pairs of particles remain fundamentally connected regardless of distance. this property enables quantum computers to perform certain calculations exponentially faster than classical computers through quantum parallelism and superdense coding."     },     {         "topic": "advanced statistics",         "question": "what is the difference between frequentist and bayesian approaches to statistical inference?",         "answer": "frequentist inference treats parameters as fixed and data as random, using probability to describe long-run frequency of events. bayesian inference treats parameters as random variables with prior distributions, updated through data to form posterior distributions, allowing direct probability statements about parameters."     },     {         "topic": "machine learning",         "question": "how do transformers solve the long-range dependency problem in sequence modeling?",         "answer": "transformers use self-attention mechanisms to directly model relationships between all positions in a sequence, eliminating the need for recurrent connections. this allows parallel processing and better capture of long-range dependencies through multi-head attention and positional encodings."     },     {         "topic": "molecular biology",         "question": "what are the implications of epigenetic inheritance for evolutionary theory?",         "answer": "epigenetic inheritance challenges the traditional neo-darwinian model by demonstrating that heritable changes in gene expression can occur without dna sequence alterations, suggesting a lamarckian component to evolution through environmentally-induced modifications."     },     {         "topic": "advanced computer architecture",         "question": "how do non-volatile memory architectures impact traditional memory hierarchy design?",         "answer": "non-volatile memory architectures blur the traditional distinction between storage and memory, enabling persistent memory systems that combine storage durability with memory-like performance, requiring fundamental redesign of memory hierarchies and system software."     } ] 

import boto3  # initialize bedrock client client = boto3.client("bedrock-runtime")  # define s3 path for your dataset dataset_path = 's3://study-materials/my-educational-dataset.json'  # fine-tune the model response = client.start_training(     modelname="gpt-3",     datasetlocation=dataset_path,     trainingparameters={"batch_size": 16, "epochs": 5} ) print(response)  

import json import boto3 from transformers import gpt2lmheadmodel, gpt2tokenizer  s3 = boto3.client('s3') model_s3_path = 's3://study-materials/fine-tuned-model'  # load model and tokenizer def load_model():     s3.download_file(model_s3_path, 'model.pth')     tokenizer = gpt2tokenizer.from_pretrained('model.pth')     model = gpt2lmheadmodel.from_pretrained('model.pth')     return tokenizer, model  tokenizer, model = load_model()  def lambda_handler(event, context):     query = event['query']     topic = event['topic']      # retrieve relevant documents from s3 (rag)     retrieved_docs = retrieve_documents_from_s3(topic)      # generate response     prompt = f"topic: {topic} question: {query} answer:"     inputs = tokenizer(prompt, return_tensors="pt")     outputs = model.generate(inputs['input_ids'], max_length=150)     answer = tokenizer.decode(outputs[0], skip_special_tokens=true)      return {         'statuscode': 200,         'body': json.dumps({'answer': answer})     }  def retrieve_documents_from_s3(topic):     # fetch study materials related to the topic from s3     # your logic for document retrieval goes here     pass  

import streamlit as st import requests  st.title("Personalized Study Companion")  topic = st.text_input("Enter Study Topic:") query = st.text_input("Enter Your Question:")  if st.button("Generate Answer"):     response = requests.post("https://your-api-endpoint", json={"topic": topic, "query": query})     answer = response.json().get("answer")     st.write(answer)