BWSK is a framework that classifies every neural network operation as S-type (information-preserving, reversible, coordination-free) or K-type (information-erasing, synchronization point) using combinator logic. This classification enables reversible backpropagation through S-phases to save memory, and CALM-based parallelism analysis.

Model Overview

Property	Value
Base Model	google-t5/t5-small
Architecture	Transformer (seq2seq)
Parameters	60M
Dataset	WikiText-2
Eval Metric	Perplexity

S/K Classification

Type	Ratio
S-type (information-preserving)	70.5%
K-type (information-erasing)	29.5%

Fine-tune Results

Mode	Final Loss	Val Perplexity	Test Perplexity	Peak Memory	Time	Epochs
Conventional	3.6739	31.64	30.62	2.2 GB	13.7m	10
BWSK Analyzed	3.7370	31.62	30.60	2.2 GB	14.7m	10
BWSK Reversible	3.5710	31.64	30.60	1.4 GB	16.2m	10

Memory savings (reversible vs conventional): 36.4%

From Scratch Results

Mode	Final Loss	Val Perplexity	Test Perplexity	Peak Memory	Time	Epochs
Conventional	4.7316	236.61	234.27	2.2 GB	14.7m	10
BWSK Analyzed	4.4787	234.60	232.10	2.2 GB	14.8m	10
BWSK Reversible	5.3226	231.85	230.42	1.4 GB	16.2m	10

Memory savings (reversible vs conventional): 36.4%

Repository Structure

├── README.md
├── results.json
├── finetune-conventional/
│   ├── model.safetensors
│   ├── config.json
│   └── training_results.json
├── finetune-bwsk-analyzed/
│   ├── model.safetensors
│   ├── config.json
│   └── training_results.json
├── finetune-bwsk-reversible/
│   ├── model.safetensors
│   ├── config.json
│   └── training_results.json
├── scratch-conventional/
│   ├── model.safetensors
│   ├── config.json
│   └── training_results.json
├── scratch-bwsk-analyzed/
│   ├── model.safetensors
│   ├── config.json
│   └── training_results.json
├── scratch-bwsk-reversible/
│   ├── model.safetensors
│   ├── config.json
│   └── training_results.json

Usage

Load a specific variant:

python

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load fine-tuned conventional variant
model = AutoModelForSeq2SeqLM.from_pretrained(
    "tzervas/bwsk-t5-small", subfolder="finetune-conventional"
)
tokenizer = AutoTokenizer.from_pretrained(
    "tzervas/bwsk-t5-small", subfolder="finetune-conventional"
)

# Load from-scratch BWSK reversible variant
model = AutoModelForSeq2SeqLM.from_pretrained(
    "tzervas/bwsk-t5-small", subfolder="scratch-bwsk-reversible"
)

Training Configuration

Setting	Value
Optimizer	AdamW
LR (fine-tune)	5e-05
LR (from-scratch)	3e-04
LR Schedule	Cosine with warmup
Max Grad Norm	1.0
Mixed Precision	AMP (float16)
Early Stopping	Patience 3
Batch Size	4
Sequence Length	512

Citation

bibtex

@software{zervas2026bwsk,
  author = {Zervas, Tyler},
  title = {BWSK: Combinator-Typed Neural Network Analysis},
  year = {2026},
  url = {https://github.com/tzervas/ai-s-combinator},
}

License

MIT

tzervas/bwsk-t5-small

BWSK T5-small

What is BWSK?