Alternatives and similar repositories for Next-Token-Failures

Users that are interested in Next-Token-Failures are comparing it to the libraries listed below

• HKUNLP / diffusion-vs-ar
  [ICLR 2025] Code for the paper "Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning"
  ☆73Updated 7 months ago
• Edward-Sun / easy-to-hard
  Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision
  ☆123Updated last year
• RLHFlow / Directional-Preference-Alignment
  Directional Preference Alignment
  ☆59Updated 11 months ago
• haozheji / exact-optimization
  ICML 2024 - Official Repository for EXO: Towards Efficient Exact Optimization of Language Model Alignment
  ☆58Updated last year
• mlfoundations / scaling
  Language models scale reliably with over-training and on downstream tasks
  ☆99Updated last year
• Leooyii / LCEG
  Long Context Extension and Generalization in LLMs
  ☆60Updated 11 months ago
• sail-sg / dice
  Official implementation of Bootstrapping Language Models via DPO Implicit Rewards
  ☆44Updated 5 months ago
• hkust-nlp / llm-compression-intelligence
  Official github repo for the paper "Compression Represents Intelligence Linearly" [COLM 2024]
  ☆141Updated 11 months ago
• ryoungj / BoLT
  Code for "Reasoning to Learn from Latent Thoughts"
  ☆118Updated 5 months ago
• ScalingIntelligence / large_language_monkeys
  ☆104Updated 11 months ago
• PKU-ML / LongPPL
  Code for ICLR 2025 Paper "What is Wrong with Perplexity for Long-context Language Modeling?"
  ☆99Updated last month
• epfml / schedules-and-scaling
  Code for NeurIPS 2024 Spotlight: "Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations"
  ☆82Updated 10 months ago
• SynthLabsAI / big-math
  A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models
  ☆63Updated 6 months ago
• RZFan525 / Awesome-ScalingLaws
  A curated list of awesome resources dedicated to Scaling Laws for LLMs
  ☆77Updated 2 years ago
• princeton-nlp / Edge-Pruning
  [NeurIPS 2024 Spotlight] Code and data for the paper "Finding Transformer Circuits with Edge Pruning".
  ☆59Updated last month
• facebookresearch / iGSM
  The code for creating the iGSM datasets in papers "Physics of Language Models Part 2.1, Grade-School Math and the Hidden Reasoning Proces…
  ☆75Updated 8 months ago
• shawntan / stickbreaking-attention
  Stick-breaking attention
  ☆60Updated 2 months ago
• janphilippfranken / sami
  Self-Supervised Alignment with Mutual Information
  ☆21Updated last year
• jihoontack / MAC
  Online Adaptation of Language Models with a Memory of Amortized Contexts (NeurIPS 2024)
  ☆67Updated last year
• yegcjs / DiffusionLLM
  Code for paper "Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning"
  ☆83Updated last year
• kamanphoebe / Look-into-MoEs
  [NAACL 2025] A Closer Look into Mixture-of-Experts in Large Language Models
  ☆54Updated 7 months ago
• qiuzh20 / gated_attention
  The official implementation for Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free
  ☆49Updated 4 months ago
• sjelassi / transformers_ssm_copy
  ☆33Updated last year
• dangxingyu / rnn-icrag
  Official repository of paper "RNNs Are Not Transformers (Yet): The Key Bottleneck on In-context Retrieval"
  ☆27Updated last year
• facebookresearch / rlfh-gen-div
  This is code for most of the experiments in the paper Understanding the Effects of RLHF on LLM Generalisation and Diversity
  ☆45Updated last year
• abhishekpanigrahi1996 / transformer_in_transformer
  ☆45Updated 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…
  ☆31Updated last year
• hamishivi / automated-instruction-selection
  Exploration of automated dataset selection approaches at large scales.
  ☆47Updated 6 months ago
• adamkarvonen / SAE_BoardGameEval
  ☆23Updated 7 months ago
• Edward-Sun / gpt-accelera
  Simple and efficient pytorch-native transformer training and inference (batched)
  ☆78Updated last year