📦 Module 8: Projects & Evaluation

[malibayram]

📦 Module 8: Projects & Evaluation

This module focuses on final projects, model evaluation, and publishing your work to showcase your LLM development skills.

Tasks to Complete:

• Lesson 8.1 — Final Project: Build and Evaluate Your Own GPT Model

  • Define final project requirements and scope
  • Guide students through project planning
  • Provide templates and starter code for final project
  • Set evaluation criteria and rubrics
  • Create project milestone checkpoints

• Lesson 8.2 — Final Project Demo: Turkish Domain-Specific LLM

  • Demonstrate building a Turkish language model
  • Show domain-specific adaptation techniques
  • Cover Turkish tokenization and preprocessing
  • Evaluate Turkish LLM performance
  • Compare with existing Turkish language models

• Lesson 8.3 — Bonus: Evaluate Tokenizer Quality & MMLU Correlation

  • Implement tokenizer evaluation metrics
  • Analyze tokenizer efficiency for different languages
  • Explore MMLU (Massive Multitask Language Understanding) benchmark
  • Study correlation between tokenization and downstream performance
  • Create tokenizer comparison tools

• Lesson 8.4 — Final Exam: MCQs and Coding Questions

  • Design comprehensive multiple-choice questions
  • Create coding challenges covering all modules
  • Implement practical programming assessments
  • Set up automated testing for coding submissions
  • Provide detailed feedback and explanations

• Lesson 8.5 — Publishing Your Model to Hugging Face

  • Create Hugging Face account and setup
  • Format model for Hugging Face Hub upload
  • Write comprehensive model documentation
  • Create model cards with evaluation results
  • Demonstrate model usage examples
  • Set up model versioning and updates

Deliverables:

• 5 video lectures (~25 minutes each)
• Final project template and guidelines
• Turkish LLM implementation example
• Tokenizer evaluation toolkit
• Comprehensive final exam
• Hugging Face publishing guide
• Certificate of completion template

Final Project Components:

• Project Requirements Document

  • Technical specifications
  • Dataset requirements
  • Minimum performance benchmarks
  • Documentation standards

• Project Templates

  • Model architecture template
  • Training pipeline template
  • Evaluation script template
  • Documentation template

• Evaluation Framework

  • Code quality assessment
  • Model performance evaluation
  • Documentation completeness
  • Innovation and creativity scores

Turkish LLM Demo:

• Turkish text preprocessing pipeline
• Turkish-specific tokenization strategy
• Domain adaptation for Turkish content
• Performance evaluation on Turkish benchmarks
• Comparison with existing Turkish models

Evaluation Tools:

• Tokenizer Analysis

  • Compression ratio calculations
  • Out-of-vocabulary analysis
  • Language-specific efficiency metrics
  • Cross-lingual tokenization comparison

• MMLU Integration

  • MMLU benchmark implementation
  • Performance correlation analysis
  • Multi-language MMLU variants
  • Benchmark result visualization

Publishing Components:

• Hugging Face model repository setup
• Model card creation and formatting
• Usage examples and documentation
• Model licensing and attribution
• Community guidelines and support

Assessment Materials:

• MCQ question bank covering all modules
• Coding challenges with automated testing
• Project rubrics and scoring guidelines
• Performance benchmarks and standards
• Certification criteria and requirements

[tllmmaster]

Hello everyone,
I have a question about training an LLM from scratch with a small dataset.
I have about 100MB of clean text data in the Turkmen language. My goal is to:

1. Pre-train a model from scratch (Next Word Prediction) using only this 100MB dataset.
2. Afterwards, perform Instruction Fine-tuning on this resulting model using the same data (converted into instruction format).
   To clarify my expectations: I only need the model to answer questions based on the information contained in this training data. I do not expect it to have general knowledge or capabilities beyond what is in the dataset.
   Is this feasible? Can I effectively get a working model by following this pipeline with such a small amount of data?