LLM Weights Transfer (Gemini)
The weights of one AI model are transferred to another model primarily through a process called Transfer Learning.
This is done by explicitly copying the numerical values of the weights (and biases) from the trained layers of a Source Model (the pre-trained model) into the corresponding layers of a Target Model (the new model).
The ability to transfer weights relies on two main factors: Compatibility and the Programming Framework's capabilities.
Same Shape (Essential): The layers from the source model and the target model must have the exact same shape for their weight tensors.
For example, if a convolutional layer in the source model has a weight matrix of shape (3, 3, 64, 128), the corresponding layer in the target model must also be able to accept a weight matrix of shape (3, 3, 64, 128).
Layer-by-Layer Mapping: The transfer is done on a layer-by-layer basis. You read the weight values from one layer in the source model and write them to a new, identically shaped layer in the target model.
In code, this often involves iterating through the layers of the source model, using a method like get_weights() or accessing the layer's state_dict, and then using a corresponding method like set_weights() or manipulating the target model's state_dict.
The Transfer Learning Process
The weights are typically transferred to leverage the knowledge gained from a large, general task (like training on millions of images) to solve a smaller, more specific task.
Load the Source Model: Load the pre-trained model and its weights.
Chop Off the Head: Remove the final output layer (or "head") of the source model. This is the task-specific layer (e.g., classifying 1000 categories).
Transfer Weights: The weights from all the early and middle layers (the feature-extraction backbone) are copied into the new target model.
Freeze the Layers: The transferred layers are typically frozen (set to trainable = False) so their weights are not updated during the subsequent training on the new task. This preserves the general features they learned.
Add a New Head: A new, randomly initialized output layer is added on top, specifically designed for the new task (e.g., classifying only 10 categories).
Train the Head: Only the new, randomly initialized layer is trained on the small dataset for the new task.
Fine-Tuning (Advanced Method)
Follow the steps (Transfer, Freeze, Add New Head).
Train: Train the new head layer for a few epochs.
Unfreeze a Few Layers: Unfreeze the original transferred layers closest to the new output layer (the middle layers). These layers hold more specialized features.
Continue Training: Continue training the entire model (the unfrozen transferred layers and the new head) using a very small learning rate.
This allows the general features to be slightly adjusted, or "fine-tuned," to the nuances of the new, specific dataset without destroying the general knowledge.
Layering is fundamental to Large Language Models (LLMs).
LLMs are a specific type of Neural Network (NN), and like all deep NNs, their architecture is defined by the organization of interconnected layers.
LLMs: A Stack of Specialized Layers
While standard neural networks (like simple feed-forward nets) use layers, LLMs use a highly specialized, repetitive structure based on the Transformer architecture.
This architecture is essentially a complex stack of two main types of blocks, each containing several layers
The Core: Transformer Blocks
An LLM is built by stacking many (often dozens or even hundreds) of identical Transformer Blocks on top of one another.
Each block is responsible for processing the input sequence and refining its representation.
A single Transformer Block is composed of two primary sub-layers
Multi-Head Attention Layer:
This is the most critical layer. It allows the model to assess the relevance of every other word in the input sequence to the current word being processed. It essentially calculates the contextual relationship between all tokens. What it does: It takes the input token representation and transforms it.
This entire process happens multiple times in parallel ("Multi-Head")
Feed-Forward Network (FFN) Layer
Purpose: This layer processes the output of the attention mechanism independently for each position in the sequence. It provides the non-linearity needed for the model to learn complex patterns.
Structure: It's typically a simple, two-layer dense (linear) network with a non-linear activation function (like GELU or ReLU) in between.
The Full Layer Stack
An LLM's full architecture involves layers before and after the main Transformer blocks:
Layer Type Location Purpose
1. Embedding Layer Input Converts input tokens (words, sub-words) into dense numerical vectors the network can process. This often includes positional encoding layers to give the model information about the order of words.
2. Transformer Blocks Middle (Deep) The core of the LLM. Hundreds of these layers extract context, semantic meaning, and grammatical relationships from the sequence.
3. Linear Layer (Head) Output Transforms the final representation produced by the Transformer Blocks into the model's desired output format, which is usually a vocabulary-sized vector (logits).
4. Softmax Layer Output Converts the logits into a probability distribution over the entire vocabulary, indicating the model's confidence for the next word prediction.