metadata
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. Try the models in the Gradio demo.