Optimizing a Transformer for a Niche Task
A practical, step-by-step example of using JAXforge to build and fine-tune a model for a specialized problem: analyzing legal documents.
The Hypothetical Problem
We need to build a Transformer model capable of Named Entity Recognition (NER) on lengthy legal contracts. The model must identify entities like "Plaintiff," "Defendant," and "Jurisdiction" with high accuracy and process documents efficiently.
Step-by-Step with JAXforge
We start in the JAX Code Generator. Instead of a generic prompt, we get specific: 'Generate a JAX/FLAX RoBERTa-style encoder block optimized for long sequences.' The AI provides a starting point with Rotary Positional Embeddings (RoPE), which is better suited for long documents than standard positional encodings.
In the LLM Configurator's AI tab, we describe our task: 'A model for NER in legal texts. Prioritize accuracy and handling sequences up to 4096 tokens.' The AI suggests a higher d_model to capture complex legal jargon and a specific number of attention heads that balances performance and granularity.
The AI's suggestion is a great start. We then switch to the 'Manual' tab to make a final adjustment. We slightly reduce the number of heads, knowing this will increase the head dimension (d_k). This is a deliberate trade-off: a larger head dimension allows the model to capture more complex relationships within a single attention head, which is crucial for dense legal text. We save this final configuration to our workspace.