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This repository collects all relevant resources about interpretability in LLMs
Neural Collapse in Multi-label Learning with Pick-all-label Loss
A curated list of papers of interesting empirical study and insight on deep learning. Continually updating...
Codebase for arXiv:2405.17767, based on GPT-Neo and TinyStories.
[NeurIPS 2021] A Geometric Analysis of Neural Collapse with Unconstrained Features
Mutual Information in Pytorch
arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv
Set of tools to assess and improve LLM security.
A Comprehensive Assessment of Trustworthiness in GPT Models
For optimization algorithm research and development.
Transformers with Arbitrarily Large Context
Finetune Llama 3, Mistral, Phi & Gemma LLMs 2-5x faster with 80% less memory
DSPy: The framework for programming—not prompting—foundation models
Modeling, training, eval, and inference code for OLMo
KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
🔥Highlighting the top ML papers every week.
A simple and efficient Mamba implementation in pure PyTorch and MLX.
the AI-native open-source embedding database
The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.
PyTorch extensions for high performance and large scale training.
A playbook for systematically maximizing the performance of deep learning models.
A framework for few-shot evaluation of language models.
A framework to optimize Parameter-Efficient Fine-Tuning for Fairness in Medical Image Analysis
Collection of papers on state-space models
The official GitHub page for the survey paper "A Survey of Large Language Models".
A repository sharing the literatures about long-context large language models, including the methodologies and the evaluation benchmarks