1. Set Up GPT-2 Medium Locally
Environment Setup:

Install Python and set up a virtual environment.
Use Hugging Face’s transformers library to load GPT-2 Medium:
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pip install transformers
Model Download and Initialization:

Load the GPT-2 Medium model and tokenizer:
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from transformers import GPT2LMHeadModel, GPT2Tokenizer

model = GPT2LMHeadModel.from_pretrained("gpt2-medium")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2-medium")
Local Inference:

Test basic local inference to ensure everything is set up correctly:
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inputs = tokenizer("Hello, how can I help you?", return_tensors="pt")
outputs = model.generate(inputs['input_ids'], max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
2. Memory Expansion Techniques
Since GPT-2 does not have memory storage built-in, we can enhance it with custom memory retention mechanisms:

Persistent Memory (Database/Long-Term Memory):

Set up a simple SQLite or JSON file to store conversation history or important user details.
Store past interactions, key preferences, and summary notes, which you can pull in as context.
Dynamic Context Windows:

Implement a rolling context window to manage input length. Keep recent conversation snippets and add relevant memories based on the current query.
3. Implementing Retrieval-Augmented Generation (RAG)
RAG combines retrieval-based techniques with language generation to pull in relevant information from a database or document store. Here’s how you can set it up:

Step 1: Set up a Vector Store:

Use libraries like FAISS (Facebook AI Similarity Search) to create embeddings of documents or past interactions and store them in a vector database. FAISS allows for efficient similarity search.
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pip install faiss-cpu
Step 2: Embedding Creation:

Use an embedding model (e.g., SentenceTransformers) to convert text into vectors:
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from sentence_transformers import SentenceTransformer
embedder = SentenceTransformer('all-MiniLM-L6-v2')
Store these embeddings alongside your documents or memories in FAISS.
Step 3: Retrieval and Incorporation:

For each user query, retrieve the top relevant documents or past interactions from FAISS.
Add this information as additional context for GPT-2 Medium to generate a more informed response.
4. Semi-Local System with Cloud Integration
For tasks that require more processing power or higher-quality model inference, consider a hybrid setup with local and cloud-based processing. Here’s how to approach it:

Local Task Management:

Use GPT-2 Medium for basic tasks, quick responses, and functions that don’t need extensive memory or context.
Store key information locally and only send essential data to the cloud for heavier processing.
Cloud Processing for Complex Tasks:

Integrate with an API (e.g., OpenAI’s GPT-4 API) for high-power tasks:
Make API calls when complex tasks, like summarization or deep analysis, are requested.
Use the locally stored information as context in the API calls to maintain continuity.
Trigger Conditions:

Define clear conditions for when to use local vs. cloud processing. For example, use local processing for lightweight Q&A and cloud processing for extended, memory-intensive tasks.
API Integration:

Example of calling an external model only when certain conditions are met:
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import openai

openai.api_key = 'YOUR_API_KEY'

def cloud_process(query):
    response = openai.Completion.create(
        engine="gpt-4",
        prompt=query,
        max_tokens=100
    )
    return response.choices[0].text.strip()


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