Official code implementation for the work Preference Alignment with Flow Matching (NeurIPS 2024)

[ICLR 2024] Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation

Modeling, training, eval, and inference code for OLMo

📰 Must-read papers on KV Cache Compression (constantly updating 🤗).

Spec-Bench: A Comprehensive Benchmark and Unified Evaluation Platform for Speculative Decoding (ACL 2024 Findings)

LLM101n: Let's build a Storyteller

Evaluation of speculative inference over multilingual tasks

Block Transformer: Global-to-Local Language Modeling for Fast Inference (NeurIPS 2024)

A curated list for Efficient Large Language Models

📰 Must-read papers and blogs on Speculative Decoding ⚡️

Official Implementation of APEX

An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.

ChatGPT, GenerativeAI and LLMs Timeline

This is the repository of HaluEval, a large-scale hallucination evaluation benchmark for Large Language Models.

Official repository of "Distort, Distract, Decode: Instruction-Tuned Model Can Refine its Response from Noisy Instructions", ICLR 2024 Spotlight

[ICML 2024] Break the Sequential Dependency of LLM Inference Using Lookahead Decoding

Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads

[IEEE Access 2022] Revisiting Orthogonality Regularization: A Study for Convolutional Neural Networks in Image Classification

[Official] NeurIPS 2023, "Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection"

Official repository of "HARE: Explainable Hate Speech Detection with Step-by-Step Reasoning", Findings of EMNLP 2023

A project page template for academic papers. Demo at https://eliahuhorwitz.github.io/Academic-project-page-template/

Implementation of Reinforcement Learning from Human Feedback (RLHF)

Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

A curated list of foundation models for vision and language tasks

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

A collection of parameter-efficient transfer learning papers focusing on computer vision and multimodal domains.

Shepherd: A foundational framework enabling federated instruction tuning for large language models