---
license: mit
library_name: pytorch
tags:
- transformer
- cipher-decoding
- byte-latent-transformer
---

# ANLP Assignment 1: Cipher-to-Text Models

Best inference checkpoints for five encoder-decoder configurations trained to reconstruct plaintext from binary ciphertext:

- C1: Base Transformer
- C2: Rotary positional embeddings (RoPE)
- C3: Grouped-query attention (GQA)
- C4: RMSNorm
- C5: Tokenizer-free byte latent transformer (BLT)

C1--C4 use the included byte-level BPE tokenizer. C5 consumes cipher bytes directly and uses entropy-based patches. Each checkpoint contains the model state and the exact architecture configuration used during training.

Training curves and evaluation results are available in the [Weights & Biases report](https://api.wandb.ai/links/vanshnawander-ocr/mgggijo5). Try the models in the [Gradio demo](https://huggingface.co/spaces/vanshnawander/anlp-assignment1-cipher-decoder-demo).