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10 Papers worth starting withOn any topic you are curious about
- Papers, essays, videos, and code - all in one place.
- Never more than 10. Just enough to get you started.
- Curated in reading order. Think it could be better? Suggest a reshuffle.
All papercases
10 itemsv2
99 papers- BitNet: Scaling 1-bit Transformers for Large Language Models
- AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
- The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
- EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
- LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
- LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
- QLoRA: Efficient Finetuning of Quantized LLMs
- SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
- ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
- 11 media
A short path into LLM quantization without a survey mountain: post-training, QAT, and 1-bit weights.
4 itemsv2
9 itemsv1
- 88 papers
- Horovod: Fast and Easy Distributed Deep Learning in TensorFlow
- PyTorch Distributed: Experiences on Accelerating Data Parallel Training
- ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
- Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
- Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM
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1 youtube- DeepMind x UCL Reinforcement Learning (2015)
11 codeCourses, lectures, and readable codebases for getting into deep RL without a paper pile.
11 docs- Large Scale Distributed Deep Networks
Data, model, and pipeline parallelism for multi-GPU training without a paper mountain.
