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	facebook/opt-350m
Architecture	Transformer (causal_lm)
Parameters	331M
Dataset	WikiText-2
Eval Metric	Perplexity

S/K Classification

Type	Ratio
S-type (information-preserving)	89.1%
K-type (information-erasing)	10.9%

Fine-tune Results

Mode	Final Loss	Val Perplexity	Test Perplexity	Peak Memory	Time	Epochs
Conventional	2.5453	15.94	15.58	8.2 GB	9.7m	3
BWSK Analyzed	2.9235	15.97	15.65	8.2 GB	13.1m	4
BWSK Reversible	2.5020	15.92	15.54	7.4 GB	11.4m	3

Memory savings (reversible vs conventional): 9.2%

From Scratch Results

Mode	Final Loss	Val Perplexity	Test Perplexity	Peak Memory	Time	Epochs
Conventional	7.6294	1714.18	1784.14	8.2 GB	19.6m	5
BWSK Analyzed	7.4390	1716.11	1786.86	8.2 GB	19.6m	5
BWSK Reversible	7.0635	1718.39	1783.87	7.4 GB	23.0m	5

Memory savings (reversible vs conventional): 9.3%

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 AutoModelForCausalLM, AutoTokenizer

# Load fine-tuned conventional variant
model = AutoModelForCausalLM.from_pretrained(
    "tzervas/bwsk-opt-350m", subfolder="finetune-conventional"
)
tokenizer = AutoTokenizer.from_pretrained(
    "tzervas/bwsk-opt-350m", subfolder="finetune-conventional"
)

# Load from-scratch BWSK reversible variant
model = AutoModelForCausalLM.from_pretrained(
    "tzervas/bwsk-opt-350m", subfolder="scratch-bwsk-reversible"
)

Training Configuration

Setting	Value
Optimizer	AdamW
LR (fine-tune)	2e-05
LR (from-scratch)	1e-04
LR Schedule	Cosine with warmup
Max Grad Norm	1.0
Mixed Precision	AMP (float16)
Early Stopping	Patience 3
Batch Size	2
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-opt-350m

BWSK OPT-350M

What is BWSK?