Project 20: Model Internals Observatory (Transformer to RLHF)

Build a comparative lab for model adaptation decisions: prompting, fine-tuning, quantization, distillation, embeddings, and multimodal capability trade-offs.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 20-35 hours
Main Programming Language Python
Alternative Programming Languages Rust, Julia
Coolness Level Level 4: Hardcore Tech Flex
Business Potential 1. The “Resume Gold”
Prerequisites ML basics, benchmarking discipline, evaluation fundamentals
Key Topics transformer internals, RLHF, fine-tuning vs prompting, quantization, distillation

1. Learning Objectives

  1. Explain transformer internals in engineering terms.
  2. Compare prompting and fine-tuning trade-offs empirically.
  3. Measure impact of quantization and distillation.
  4. Evaluate embedding model differences for retrieval tasks.
  5. Build a decision matrix for adaptation strategy selection.

2. Theoretical Foundation

2.1 Model Adaptation Spectrum

Prompting is fast and flexible but can be brittle for narrow high-precision domains. Fine-tuning can improve consistency but adds training and maintenance costs. Quantization reduces resource needs but can hurt edge-case reasoning. Distillation speeds inference but may lose nuanced capabilities. These are strategic product decisions, not just model tricks.

2.2 Alignment and Continual Learning

RLHF improves helpfulness and safety characteristics but can introduce preference bias. Continual adaptation must avoid catastrophic forgetting and should be accompanied by regression evals.

3. Project Specification

3.1 What You Will Build

A model literacy lab with:

  • benchmark task set
  • adaptation strategy runner
  • compression comparison suite
  • embedding comparison harness
  • summary report generator

3.2 Functional Requirements

  1. Run baseline prompting benchmarks.
  2. Compare at least one fine-tuned variant.
  3. Test at least one quantized and one distilled model.
  4. Benchmark multiple embedding models for retrieval tasks.
  5. Generate strategy recommendation report.

3.3 Non-Functional Requirements

  • Reproducibility: fixed task sets and seeds.
  • Interpretability: per-strategy metric breakdown.
  • Practicality: include cost and latency metrics.

3.4 Real World Outcome

$ modellab compare --task-set assistant_core_v2
[Prompting] success=0.81 cost=$0.014 latency=1.1s
[FineTune] success=0.87 cost=$0.009 latency=0.9s
[Quantized-4bit] success=0.83 cost=$0.004 latency=0.6s
[Distilled] success=0.79 cost=$0.003 latency=0.4s
[Recommendation] choose FineTune for stable domain, Prompting for rapid iteration

4. Solution Architecture

4.1 High-Level Design

Task Set -> Strategy Runner -> Metrics Collector -> Comparator -> Decision Report

4.2 Key Components

Component Responsibility Key Decisions
Strategy runner execute model variants same tasks across variants
Metrics collector quality/cost/latency unified schema
Comparator compute deltas significance thresholds
Reporter actionable recommendation product-context weighting

5. Implementation Guide

5.1 The Core Question You’re Answering

“Which model adaptation strategy best matches my assistant’s constraints and why?”

5.2 Concepts You Must Understand First

  1. Transformer architecture basics
  2. RLHF objective intuition
  3. Compression trade-offs
  4. Benchmark design principles

5.3 Questions to Guide Your Design

  1. Which tasks are unstable under prompting?
  2. What quality drop is acceptable for speed gains?
  3. Which embedding model gives better retrieval precision?

5.4 Thinking Exercise

Create a strategy matrix for prototype, SMB SaaS, and regulated enterprise deployments.

5.5 The Interview Questions They’ll Ask

  1. Explain attention in practical terms.
  2. What does RLHF optimize?
  3. When is fine-tuning worth it?
  4. Quantization versus distillation: key differences?
  5. How do you prevent catastrophic forgetting in iterative updates?

5.6 Hints in Layers

Hint 1: establish baseline metrics first.

Hint 2: compare one variable at a time.

Hint 3: keep evaluation tasks fixed across runs.

Hint 4: tie recommendations to product constraints, not abstract scores.

5.7 Books That Will Help

Topic Book Chapter
Transformer internals “Build a Large Language Model (From Scratch)” Ch. 2-5
Applied trade-offs “AI Engineering” model/deployment chapters
Eval rigor “The LLM Engineering Handbook” evaluation chapters

5.8 Common Pitfalls and Debugging

Problem 1: misleading benchmark conclusions

  • Why: task set too narrow.
  • Fix: add diverse capability categories.
  • Quick test: per-category metrics are stable and interpretable.

Problem 2: compression harms critical edge cases

  • Why: only average metrics tracked.
  • Fix: monitor worst-case task buckets separately.
  • Quick test: hard-task subset maintains minimum threshold.

5.9 Definition of Done

  • Comparative benchmark spans at least four adaptation strategies
  • Report includes quality, cost, latency, and risk trade-offs
  • Strategy recommendations are scenario-specific
  • Findings are reproducible and versioned