Juniper2.0

README.md

💡 Project: Building a Small Conversational LLM Based on Juniper

• Main source: OpenAI GPT-2 (base), -4 (Juniper)
• Languages: Python, JSON

🏆 Goal

Develop a conversational LLM model based on my personalized GPT-4 model, Juniper. The model will be able to:

• Interact with users and answer questions.
• Provide coding and other tech-related lessons and examples.
• Assess the user's knowledge through quizzes.
• Offer career guidance (interview prep, resume-building, job application tracking, send reminders, and job sourcing).

This model is designed to be a learning tool for beginners to start a career in tech, with a target audience that includes:

• Continuing adult education learners.
• Prisoners or individuals with criminal records.
• GED students.
• Those experiencing financial instability or hardship.
• Other disadvantaged, novice, or late learners.

✳️ Description

Juniper 2.0 is an LLM based on OpenAI's GPT-2 model and my interactions with my assistant, Juniper (based on the GPT-4 model). This personalized assistant is built to simplify complex tech concepts and provide clear, easy-to-understand responses.

🏈 Game Plan

Step 1: Project setup

• Install PyTorch and Hugging Face Transformers packages ☑️
• Test basic functionality ☑️
• Source base model dataset:
  • OpenAI GPT-2 (open source) 💬
    • Conversational
    • Provides simplified responses to complex tech concepts

Step 2: Data Collection/Preparation

• Collect/Create Datasets:

  • Combine the following datasets:
    • OpenAI GPT-2 (base) 💬
      • Conversational tone/context
    • StackExchange
      • Professional tone/context
      • Tech-related conversations (Q&A)
    • Kaggle
      • Tech knowledge and factoids
      • Tutorials
      • Coding examples
      • Quizzes
    • GitHub Jobs/LinkedIn Jobs/Indeed APIs
      • Job sourcing and career guidance
      • Resume-building
      • Interview prep
      • Job application tracking/reminders
    • Custom Dataset
      • Samples of conversations between Juniper (GPT-4) and myself

• Data Preprocessing:

  • Format and clean the datasets
  • Tokenize using mySQL/Excel/VSCode
  • Store datasets as JSON or CSV with
    'input_text' and 'output_text'

Step 3: Fine-Tuning

• Use the Hugging Face Transformers library for fine-tuning

Step 4: Model Evaluation and Tuning

• Evaluate the model's performance.
• Optimize the model for:
  • Accuracy
  • Response quality
  • Data-specific goals

Step 5: Deployment

• Build an API for access (FastAPI/Flask).
• Deploy onto Hugging Face model hub and GitHub.

License

This project is licensed under the GNU General Public License v3.0. See the LICENSE file for details.

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