Alternatives and similar repositories for seq_icl

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

• sustcsonglin / mamba-triton
  ☆49Updated last year
• HazyResearch / zoology
  Understand and test language model architectures on synthetic tasks.
  ☆222Updated last month
• jopetty / word-problem
  Experiments on the impact of depth in transformers and SSMs.
  ☆33Updated 9 months ago
• JonasGeiping / linear_cross_entropy_loss
  A fusion of a linear layer and a cross entropy loss, written for pytorch in triton.
  ☆70Updated last year
• epfml / schedules-and-scaling
  Code for NeurIPS 2024 Spotlight: "Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations"
  ☆81Updated 9 months ago
• Edward-Sun / gpt-accelera
  Simple and efficient pytorch-native transformer training and inference (batched)
  ☆78Updated last year
• athms / mad-lab
  A MAD laboratory to improve AI architecture designs 🧪
  ☆125Updated 8 months ago
• shawntan / stickbreaking-attention
  Stick-breaking attention
  ☆59Updated last month
• EleutherAI / nanoGPT-mup
  The simplest, fastest repository for training/finetuning medium-sized GPTs.
  ☆152Updated last month
• RobertCsordas / moeut
  ☆85Updated last year
• srush / mamba-primer
  ☆37Updated last year
• mlfoundations / scaling
  Language models scale reliably with over-training and on downstream tasks
  ☆98Updated last year
• epfml / llm-baselines
  nanoGPT-like codebase for LLM training
  ☆102Updated 3 months ago
• sjelassi / transformers_ssm_copy
  ☆33Updated last year
• kyo-takano / chinchilla
  A toolkit for scaling law research ⚖
  ☆50Updated 6 months ago
• shikaiqiu / compute-better-spent
  ☆56Updated 10 months ago
• mnoukhov / async_rlhf
  Code and Configs for Asynchronous RLHF: Faster and More Efficient RL for Language Models
  ☆60Updated 4 months ago
• dangxingyu / rnn-icrag
  Official repository of paper "RNNs Are Not Transformers (Yet): The Key Bottleneck on In-context Retrieval"
  ☆27Updated last year
• HazyResearch / based
  Code for exploring Based models from "Simple linear attention language models balance the recall-throughput tradeoff"
  ☆241Updated 2 months ago
• abhishekpanigrahi1996 / transformer_in_transformer
  ☆45Updated last year
• wesg52 / universal-neurons
  Universal Neurons in GPT2 Language Models
  ☆30Updated last year
• martin-marek / batch-size
  📄Small Batch Size Training for Language Models
  ☆42Updated 2 weeks ago
• insuhan / hyper-attn
  ☆81Updated last year
• gregorbachmann / Next-Token-Failures
  ☆91Updated last year
• McGill-NLP / length-generalization
  Code for the paper "The Impact of Positional Encoding on Length Generalization in Transformers", NeurIPS 2023
  ☆136Updated last year
• amirzandieh / HyperAttention
  Triton Implementation of HyperAttention Algorithm
  ☆48Updated last year
• lucidrains / pause-transformer
  Yet another random morning idea to be quickly tried and architecture shared if it works; to allow the transformer to pause for any amount…
  ☆53Updated last year
• samsja / muon_fsdp_2
  Muon fsdp 2
  ☆39Updated 2 weeks ago
• JeanKaddour / NoTrainNoGain
  Revisiting Efficient Training Algorithms For Transformer-based Language Models (NeurIPS 2023)
  ☆81Updated last year
• PiotrNawrot / nano-sparse-attention
  The simplest implementation of recent Sparse Attention patterns for efficient LLM inference.
  ☆84Updated last month