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  • Reference implementation for DPO (Direct Preference Optimization)

    Python 1,966 158 Apache License 2.0 Updated Aug 11, 2024
  • An Emulator for Fine-tuning Large Language Models using Small Language Models

    10 1 Apache License 2.0 Updated Apr 14, 2024
  • 2 Updated Feb 22, 2024
  • mend Public

    MEND: Fast Model Editing at Scale

    Python 229 32 MIT License Updated Aug 30, 2023
  • detect-gpt Public

    DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature

    Python 344 52 MIT License Updated Mar 4, 2023
  • dart Public

    Deep Autoregressive Tensor Trains

    Python 1 Updated Dec 8, 2022
  • macaw Public

    Code for "Offline Meta-Reinforcement Learning with Advantage Weighting" [ICML 2021]

    Python 46 11 Updated Nov 30, 2022
  • HTML MIT License Updated Nov 22, 2022
  • concord Public

    Jupyter Notebook 9 5 Updated Nov 15, 2022
  • serac Public

    Semi-Parametric Editing with a Retrieval-Augmented Counterfactual Model

    Python 58 6 MIT License Updated Nov 1, 2022
  • Python Apache License 2.0 Updated May 6, 2022
  • higher Public

    Forked from facebookresearch/higher

    higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.

    Python 1 Apache License 2.0 Updated Apr 27, 2022
  • macaw-min Public

    Clean, extensible implementation of MACAW [ICML 2021]

    Python 10 4 Updated Dec 7, 2021
  • 🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.

    Python Apache License 2.0 Updated Sep 23, 2021
  • Bare project template with basic Guild/Torch config

    Python Updated Jul 21, 2021
  • Code for Editing Factual Knowledge in Language Models

    Python MIT License Updated Jul 2, 2021
  • Python Updated Nov 6, 2020
  • akima Public

    Forked from rzwink/akima

    Article extraction and annotation.

    HTML Updated Sep 5, 2020
  • metaworld Public

    Python Updated Aug 2, 2020
  • oyster Public

    Forked from katerakelly/oyster

    Implementation of Efficient Off-policy Meta-learning via Probabilistic Context Variables (PEARL)

    Python MIT License Updated Jan 10, 2020
  • Reimplementation of World-Models (Ha and Schmidhuber 2018) in pytorch

    Python MIT License Updated Sep 3, 2019