Research Experiments

This document details the experiments conducted to evaluate BSBR's performance and capabilities.

Experimental Setup

PyTorch (e.g., 2.x)
CUDA (If GPU used)
Python 3.12
BSBR 0.1.2
Key libraries: transformers, numpy, pandas, matplotlib, seaborn (see requirements.txt)

Experiments typically compare BSBR against:

See Benchmarks for details on architectures.

Experiments measure inference time and memory usage across varying sequence lengths (e.g., 64 to 1024 or higher).

Objective: Determine empirical scaling behavior (e.g., O(n), O(n log n), O(n^2)).
Method: Run research/architecture_comparisons/compare_models.py followed by research/architecture_comparisons/analyze_results.py.
Results: See detailed tables and plots in Benchmarks - Performance.

(Placeholder: Describes potential training experiments)

Compare training loss curves and epochs required to reach a target validation metric for BSBR vs. baselines.

Measure peak GPU/CPU memory usage during training, including gradients and activations.

Evaluate language modeling capability using perplexity on datasets like Wikitext.

Objective: Assess base model quality after architectural changes (e.g., BSBR conversion).
Method: Run research/conversion_experiments/benchmark_comparison.py --quality_eval.
Results: See BSBR Conversion Evaluation - Perplexity.

Quantify how closely the outputs (hidden states, token predictions) of a modified model (e.g., BSBR-converted) match the original.

Objective: Understand the behavioral impact of architectural changes like BSBR conversion.
Method: Run research/conversion_experiments/output_comparison.py.
Metrics: Cosine similarity, MSE, KL divergence (hidden states); Top-K agreement rates (predictions).
Results: See detailed metrics in BSBR Conversion Evaluation - Output Similarity.

(Placeholder: Describes potential task-based evaluations)

Evaluate performance on tasks requiring reasoning over long contexts (e.g., document classification, QA over long passages).

(Placeholder: Describes potential attention pattern analysis)

Visualize attention maps to understand how BSBR focuses on different parts of the context compared to standard attention.

(Placeholder: Describes potential ablation studies)

Evaluate how varying the chunk_size in BSBR affects performance, memory, and potentially task accuracy.

Analyze the trade-off between state compression in BSBR (memory/speed) and potential impact on model quality.

(Placeholder: Describes potential application-specific tests)

Test BSBR in simulated real-world scenarios like processing large documents or handling long conversational contexts.