Alternatives and similar repositories for Expressive_Power_of_LoRA

Users that are interested in Expressive_Power_of_LoRA are comparing it to the libraries listed below

• ethz-spylab / superhuman-ai-consistency
  ☆29Updated 2 years ago
• abhishekpanigrahi1996 / transformer_in_transformer
  ☆45Updated last year
• formll / resolving-scaling-law-discrepancies
  ☆20Updated last year
• dangxingyu / rnn-icrag
  Official repository of paper "RNNs Are Not Transformers (Yet): The Key Bottleneck on In-context Retrieval"
  ☆27Updated last year
• allenbai01 / transformers-as-statisticians
  ☆33Updated 2 years ago
• cassidylaidlaw / orpo
  ☆18Updated 9 months ago
• ethancaballero / broken_neural_scaling_laws
  Code Release for "Broken Neural Scaling Laws" (BNSL) paper
  ☆59Updated last year
• srush / mamba-scans
  Blog post
  ☆17Updated last year
• MadryLab / modeldiff
  ModelDiff: A Framework for Comparing Learning Algorithms
  ☆59Updated 2 years ago
• yidingjiang / ado
  The repository contains code for Adaptive Data Optimization
  ☆25Updated 8 months ago
• mcleish7 / gemstone-scaling-laws
  ☆28Updated 6 months ago
• ctlllll / reward_collapse
  ☆27Updated 2 years ago
• janphilippfranken / sami
  Self-Supervised Alignment with Mutual Information
  ☆21Updated last year
• xiamengzhou / training_trajectory_analysis
  [ACL 2023]: Training Trajectories of Language Models Across Scales https://arxiv.org/pdf/2212.09803.pdf
  ☆24Updated last year
• nathanhu0 / CaMeLS
  Codebase for Context-aware Meta-learned Loss Scaling (CaMeLS). https://arxiv.org/abs/2305.15076.
  ☆25Updated last year
• gregorbachmann / Next-Token-Failures
  ☆98Updated last year
• IBM / ColPret
  Efficient Scaling laws and collaborative pretraining.
  ☆18Updated 7 months ago
• hamishivi / automated-instruction-selection
  Exploration of automated dataset selection approaches at large scales.
  ☆47Updated 6 months ago
• bdusell / stack-attention
  Code for the paper "Stack Attention: Improving the Ability of Transformers to Model Hierarchical Patterns"
  ☆17Updated last year
• Asap7772 / understanding-rlhf
  Learning from preferences is a common paradigm for fine-tuning language models. Yet, many algorithmic design decisions come into play. Ou…
  ☆30Updated last year
• dunzeng / MORE
  Code for EMNLP'24 paper - On Diversified Preferences of Large Language Model Alignment
  ☆16Updated last year
• ylsung / vl-merging
  PyTorch codes for the paper "An Empirical Study of Multimodal Model Merging"
  ☆37Updated last year
• p-lambda / incontext-learning
  Experiments and code to generate the GINC small-scale in-context learning dataset from "An Explanation for In-context Learning as Implici…
  ☆108Updated last year
• pietrolesci / memorisation-profiles
  This is the official implementation for our ACL 2024 paper: "Causal Estimation of Memorisation Profiles".
  ☆23Updated 5 months ago
• nrimsky / InfluenceFunctions
  Implementation of Influence Function approximations for differently sized ML models, using PyTorch
  ☆15Updated last year
• ekinakyurek / google-research
  Google Research
  ☆45Updated 2 years ago
• yikangshen / megablocks
  ☆20Updated last year
• MadryLab / datamodels-data
  Data for "Datamodels: Predicting Predictions with Training Data"
  ☆97Updated 2 years ago
• snap-stanford / zeroc
  ZeroC is a neuro-symbolic method that trained with elementary visual concepts and relations, can zero-shot recognize and acquire more com…
  ☆32Updated 2 years ago
• rtaori / data_feedback
  Code for the paper "Data Feedback Loops: Model-driven Amplification of Dataset Biases"
  ☆16Updated 2 years ago