dmis-lab / Outlier-Safe-Pre-TrainingLinks
[ACL 2025] Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models
β31Updated 2 months ago
Alternatives and similar repositories for Outlier-Safe-Pre-Training
Users that are interested in Outlier-Safe-Pre-Training are comparing it to the libraries listed below
Sorting:
- πSmall Batch Size Training for Language Modelsβ63Updated last week
- β48Updated last year
- β53Updated last year
- Flash-Muon: An Efficient Implementation of Muon Optimizerβ193Updated 4 months ago
- Tree Attention: Topology-aware Decoding for Long-Context Attention on GPU clustersβ130Updated 10 months ago
- β85Updated last year
- A MAD laboratory to improve AI architecture designs π§ͺβ129Updated 9 months ago
- The evaluation framework for training-free sparse attention in LLMsβ101Updated 3 months ago
- Pytorch implementation of the PEER block from the paper, Mixture of A Million Experts, by Xu Owen He at Deepmindβ129Updated last year
- β91Updated last year
- The simplest, fastest repository for training/finetuning medium-sized GPTs.β164Updated 3 months ago
- Experiment of using Tangent to autodiff tritonβ80Updated last year
- Triton Implementation of HyperAttention Algorithmβ48Updated last year
- Here we will test various linear attention designs.β61Updated last year
- Muon fsdp 2β44Updated 2 months ago
- Accelerated First Order Parallel Associative Scanβ189Updated last year
- Understand and test language model architectures on synthetic tasks.β231Updated 3 weeks ago
- Code for exploring Based models from "Simple linear attention language models balance the recall-throughput tradeoff"β240Updated 4 months ago
- Mixture of A Million Expertsβ48Updated last year
- JAX bindings for Flash Attention v2β96Updated last month
- Yet another random morning idea to be quickly tried and architecture shared if it works; to allow the transformer to pause for any amountβ¦β52Updated last year
- Minimal (400 LOC) implementation Maximum (multi-node, FSDP) GPT trainingβ132Updated last year
- The source code of our work "Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models" [AISTATS β¦β60Updated last year
- Code for NeurIPS 2024 Spotlight: "Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations"β84Updated 11 months ago
- supporting pytorch FSDP for optimizersβ83Updated 10 months ago
- β46Updated last year
- Simple and efficient pytorch-native transformer training and inference (batched)β78Updated last year
- Normalized Transformer (nGPT)β191Updated 10 months ago
- Fast and memory-efficient exact attentionβ70Updated 7 months ago
- β58Updated last year