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1. Code and documentation to train Stanford's Alpaca models, and generate the data.

   Python 30.2k 4k

2. An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.

   Jupyter Notebook 2k 315

3. Experiments for understanding disentanglement in VAE latent representations

   Python 844 150

4. Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.

   Jupyter Notebook 239 52

5. Generic image compressor for machine learning. Pytorch code for our paper "Lossy compression for lossless prediction".

   Python 121 11

6. Pytorch code for "Improving Self-Supervised Learning by Characterizing Idealized Representations"

   Python 41 7