Instructions to use SinisterLlama/anlp-a1-c5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SinisterLlama/anlp-a1-c5 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SinisterLlama/anlp-a1-c5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Config 5: Byte Latent Transformer (BLT) - Final 60 Epochs
This model is part of the ANLP Assignment 1: Custom Transformers & Byte Latent Transformers at IIIT Hyderabad.
Architecture
- Description: Byte Latent Transformer with entropy-based dynamic patching, local byte encoder/decoder, global latent cross-attention transformer.
- Parameters: 8.48M
Test Evaluation Results (Epoch 60)
- Bit-Level Accuracy: 98.73%
- Sequence Accuracy: 87.12%
- Average Levenshtein Distance: 0.45
- BLEU-4 Score: 96.17%
- ROUGE-1 / ROUGE-2 / ROUGE-L: 98.07% / 95.77% / 98.07%
Repository & Links
- GitHub: https://github.com/SinisterLlamma/ANLP-Assignment1
- Author: Eshaan Sharma (SinisterLlama)
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