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Who's Who: Large Language Models Meet Knowledge Conflicts in Practice (EMNLP 2024 Findings)
Generative Representational Instruction Tuning
LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance.
Simple and efficient pytorch-native transformer text generation in <1000 LOC of python.
A resource repository for machine unlearning in large language models
LongProc: Benchmarking Long-Context Language Models on Long Procedural Generation
Awesome LLM compression research papers and tools.
A guidance language for controlling large language models.
[ICLR 2024] SWE-bench: Can Language Models Resolve Real-world Github Issues?
[ICLR 2025] LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs
CLI & Python API to easily summarize text-based files with transformers
[ACL 2024] Long-Context Language Modeling with Parallel Encodings
The code for the paper: "Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models"
The related works and background techniques about Openai o1
AnchorAttention: Improved attention for LLMs long-context training
The Official Implementation of Ada-KV: Optimizing KV Cache Eviction by Adaptive Budget Allocation for Efficient LLM Inference
Updating collection of summarization datasets in 100+ languages, based on our survey "The State and Fate of Summarization Datasets".
This repo contains the source code for RULER: What’s the Real Context Size of Your Long-Context Language Models?
Data and code for our paper "Why Does the Effective Context Length of LLMs Fall Short?"
The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.
📰 Must-read papers on KV Cache Compression (constantly updating 🤗).
Homepage for ProLong (Princeton long-context language models) and paper "How to Train Long-Context Language Models (Effectively)"
[ICLR2025] MagicPIG: LSH Sampling for Efficient LLM Generation
Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks [ICLR 2025]