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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).
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