Understanding Weights, Architecture, and Fine-Tuning in LLMs

1. Architecture vs Weights §

Term	Definition	Analogy
Architecture	Blueprint or design of the model; defines layers, connections, and data flow	Building blueprint: how floors and rooms connect
Weights	Learned numeric parameters controlling neuron influence	Volume knobs on each musician, sheet music telling them which notes to emphasize

2. How They Work Together §

flowchart TD
    Input[Text Input] --> Layer1[Layer 1 Neurons]
    Layer1 --> Layer2[Layer 2 Neurons]
    Layer2 --> Layer3[Layer 3 Neurons]
    Layer3 --> Output[Predicted Output]
    style Layer1 fill:#f9f,stroke:#333,stroke-width:2px
    style Layer2 fill:#9ff,stroke:#333,stroke-width:2px
    style Layer3 fill:#ff9,stroke:#333,stroke-width:2px

The architecture defines the structure of layers (e.g., Layer1 → Layer2 → Layer3), while weights are the numbers controlling the influence between neurons in each layer.

3. Open Weights Example §

# Tiny illustrative snippet
layer1.weight = [
  [0.012, -0.054, 0.233, 0.001],
  [-0.112, 0.342, -0.431, 0.119],
  [0.003, -0.004, 0.002, -0.001]
]
 
layer1.bias = [0.001, -0.002, 0.003]

Real models have billions of weights (e.g., GPT-3 has 175B).
Weights encode learned patterns; architecture defines their interaction.

4. Fine-Tuning §

Manual weight adjustments are impossible due to the sheer size of models.

The fine-tuning process involves:

• Providing examples (input/output pairs)
• The model predicts an output
• Calculating the error between predicted and actual output
• Backpropagation automatically adjusts weights based on the error

Approaches to fine-tuning include:

• Full fine-tuning: All weights are adjusted (requires high compute).
• Parameter-efficient fine-tuning (PEFT): Techniques like LoRA, adapters, and prefix-tuning adjust only a small subset of parameters.
• Prompt engineering: Crafting better inputs to guide the model, without changing any weights.

5. Orchestra Analogy §

• Neurons = individual musicians
• Layers = sections of the orchestra (strings, brass, woodwinds)
• Weights = volume knobs on each musician’s instrument + the sheet music telling them which notes to emphasize
• Fine-tuning = a conductor adjusting volume and emphasis for specific parts of a piece
• Prompt engineering = giving the musicians better sheet music or clearer instructions instead of touching their individual knobs

Optimization algorithms (like Adam or SGD) automatically figure out which “knobs” to tweak during training.

6. Key Takeaways §

• Weights and architecture are inseparable for a functional model.
• Open weights allow for fine-tuning and adaptation, but full replication of a model’s capabilities often requires its original training data.
• Fine-tuning enables you to specialize huge pre-trained models for specific tasks without manually adjusting billions of parameters.