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Neural Machine Translation of Rare Words with Subword Units ↗
Rico Sennrich, Barry Haddow, Alexandra Birch · Published: 2016-08 · Accessed: 2026-11-13
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Used in: Tokens: the pieces a model reads · Tokenization: choosing the alphabet
Embedding — PyTorch 2.15 documentation ↗
PyTorch contributors · Published: Version 2.15; accessed 2026-11-13 · Accessed: 2026-11-13
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Used in: Tokens: the pieces a model reads · Embeddings: giving tokens coordinates
Neural Networks: Zero to Hero ↗
Andrej Karpathy · Published: Living course; accessed 2026-11-13 · Accessed: 2026-11-13
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Used in: Tokenization: choosing the alphabet · Neural networks: composing simple calculations
SentencePiece ↗
Taku Kudo, John Richardson · Published: 2018-11 · Accessed: 2026-11-13
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Used in: Tokenization: choosing the alphabet
minbpe: byte-pair encoding reference implementation ↗
Andrej Karpathy · Published: Living repository; accessed 2026-11-13 · Accessed: 2026-11-13
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Used in: Tokenization: choosing the alphabet
The Pile: An 800GB Dataset of Diverse Text for Language Modeling ↗
Leo Gao et al., EleutherAI · Published: 2020-12-31 · Accessed: 2026-11-13
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Deduplicating Training Data Makes Language Models Better ↗
Katherine Lee et al. · Published: 2022-03-24 (v2) · Accessed: 2026-11-13
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Efficient Estimation of Word Representations in Vector Space ↗
Tomas Mikolov et al. · Published: 2013-09-07 (v3) · Accessed: 2026-11-13
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Attention Is All You Need ↗
Ashish Vaswani et al., Google and University of Toronto · Published: 2017; revised 2023-08-02 (v7) · Accessed: 2026-11-13
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Used in: Positions: giving order to the sequence · Transformers: a repeated pattern of computation · Attention heads: several mixtures in parallel
RoFormer: Enhanced Transformer with Rotary Position Embedding ↗
Jianlin Su et al. · Published: 2021; revised 2023-11-08 (v5) · Accessed: 2026-11-13
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Linear — PyTorch 2.15 documentation ↗
PyTorch contributors · Published: Version 2.15; accessed 2026-11-13 · Accessed: 2026-11-13
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Used in: Parameters: the numbers learning changes · Neural networks: composing simple calculations
nanoGPT: historical GPT implementation ↗
Andrej Karpathy and contributors · Published: Historical implementation; repository accessed 2026-11-13 · Accessed: 2026-11-13
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Used in: Parameters: the numbers learning changes · Transformers: a repeated pattern of computation
Layer Normalization ↗
Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton · Published: 2016-07-21 · Accessed: 2026-11-13
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Used in: Transformers: a repeated pattern of computation · The residual stream: updating a running representation
Scaled dot-product attention — PyTorch 2.15 documentation ↗
PyTorch contributors · Published: Version 2.15; accessed 2026-11-13 · Accessed: 2026-11-13
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Used in: Attention: a computed weighted mixture · Causal masking: no peeking at the answer
Attention is not Explanation ↗
Sarthak Jain, Byron C. Wallace · Published: 2019-05-08 (v3) · Accessed: 2026-11-13
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Are Sixteen Heads Really Better than One? ↗
Paul Michel, Omer Levy, Graham Neubig · Published: 2019-11-04 (v3) · Accessed: 2026-11-13
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GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints ↗
Joshua Ainslie et al. · Published: 2023-12-23 (v3) · Accessed: 2026-11-13
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Used in: Attention heads: several mixtures in parallel · The KV cache: reusing the past
GLU Variants Improve Transformer ↗
Noam Shazeer · Published: 2020-02-12 · Accessed: 2026-11-13
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Transformer Feed-Forward Layers Are Key-Value Memories ↗
Mor Geva, Roei Schuster, Jonathan Berant, Omer Levy · Published: 2021-09-05 (v2) · Accessed: 2026-11-13
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Deep Residual Learning for Image Recognition ↗
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun · Published: 2015-12-10 · Accessed: 2026-11-13
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Used in: The residual stream: updating a running representation
A Mathematical Framework for Transformer Circuits ↗
Nelson Elhage et al., Anthropic · Published: 2021-12-22 · Accessed: 2026-11-13
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Used in: The residual stream: updating a running representation
Using the Output Embedding to Improve Language Models ↗
Ofir Press, Lior Wolf · Published: 2017-02-21 (v3) · Accessed: 2026-11-13
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Used in: Logits: scores before probabilities
Softmax — PyTorch 2.15 documentation ↗
PyTorch contributors · Published: Version 2.15; accessed 2026-11-13 · Accessed: 2026-11-13
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Used in: Logits: scores before probabilities · Softmax: turning scores into a distribution
On Calibration of Modern Neural Networks ↗
Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger · Published: 2017-08-03 (v2) · Accessed: 2026-11-13
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Generation configuration — Transformers documentation ↗
Hugging Face contributors · Published: Main documentation; accessed 2026-11-13 · Accessed: 2026-11-13
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Used in: Temperature: reshaping the odds
Generation strategies — Transformers documentation ↗
Hugging Face contributors · Published: Main documentation; accessed 2026-11-13 · Accessed: 2026-11-13
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Used in: Sampling: choosing the next token · Autoregression: one choice becomes the next input
The Curious Case of Neural Text Degeneration ↗
Ari Holtzman et al. · Published: 2020-02-14 (v2) · Accessed: 2026-11-13
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Used in: Sampling: choosing the next token
Language Models are Few-Shot Learners ↗
Tom B. Brown et al., OpenAI · Published: 2020-07-22 (v4) · Accessed: 2026-11-13
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Used in: Next-token prediction: a small objective with broad demands · Pretraining: learning from continuations · In-context learning: adapting without weight updates
How caching works — Transformers documentation ↗
Hugging Face contributors · Published: Main documentation; accessed 2026-11-13 · Accessed: 2026-11-13
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Used in: Autoregression: one choice becomes the next input · The KV cache: reusing the past
Lost in the Middle: How Language Models Use Long Contexts ↗
Nelson F. Liu et al. · Published: 2023-11-20 (v3) · Accessed: 2026-11-13
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Used in: Context windows: what is available right now · Context management: choosing what stays in view
Fast Transformer Decoding: One Write-Head is All You Need ↗
Noam Shazeer · Published: 2019-11-06 · Accessed: 2026-11-13
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Used in: The KV cache: reusing the past
CrossEntropyLoss documentation ↗
PyTorch contributors · Published: Living documentation; checked 2026-11-13 · Accessed: 2026-11-13
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Used in: Loss: a number to learn from
Deep Learning, chapter 8: Optimization ↗
Ian Goodfellow, Yoshua Bengio and Aaron Courville · Published: 2016 · Accessed: 2026-11-13
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Used in: Loss: a number to learn from · Gradient descent: changing the weights · Generalization: beyond the examples seen
Automatic differentiation with torch.autograd ↗
PyTorch contributors · Published: Living documentation; checked 2026-11-13 · Accessed: 2026-11-13
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Scaling Laws for Neural Language Models ↗
Jared Kaplan et al. · Published: 2020-01-23 · Accessed: 2026-11-13
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Used in: Scaling laws: trends, not guarantees
Training Compute-Optimal Large Language Models ↗
Jordan Hoffmann et al. · Published: 2022-03-29 · Accessed: 2026-11-13
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Used in: Scaling laws: trends, not guarantees · Compute: the work behind the words
Predictability and Surprise in Large Generative Models ↗
Deep Ganguli et al. · Published: 2022-02-15 · Accessed: 2026-11-13
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Used in: Scaling laws: trends, not guarantees · Generalization: beyond the examples seen
Training language models to follow instructions with human feedback ↗
Long Ouyang et al. · Published: 2022-03-04 · Accessed: 2026-11-13
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Used in: Supervised fine-tuning: demonstrations of behavior · RLHF: optimizing a learned reward
Multitask Prompted Training Enables Zero-Shot Task Generalization ↗
Victor Sanh et al. · Published: 2021-10-15 · Accessed: 2026-11-13
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Used in: Supervised fine-tuning: demonstrations of behavior · Generalization: beyond the examples seen
Deep reinforcement learning from human preferences ↗
Paul Christiano et al. · Published: 2017-06-12 · Accessed: 2026-11-13
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Constitutional AI: Harmlessness from AI Feedback ↗
Yuntao Bai et al. · Published: 2022-12-15 · Accessed: 2026-11-13
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Proximal Policy Optimization Algorithms ↗
John Schulman et al. · Published: 2017-07-20 · Accessed: 2026-11-13
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Used in: RLHF: optimizing a learned reward
Direct Preference Optimization: Your Language Model is Secretly a Reward Model ↗
Rafael Rafailov et al. · Published: 2023-05-29; revision 2024-07-29 · Accessed: 2026-11-13
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Llama 2: Open Foundation and Fine-Tuned Chat Models ↗
Hugo Touvron et al. · Published: 2023-07-18 · Accessed: 2026-11-13
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Used in: Base model versus assistant
Chat templates ↗
Hugging Face contributors · Published: Living documentation; checked 2026-11-13 · Accessed: 2026-11-13
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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks ↗
Patrick Lewis et al. · Published: 2020-05-22 · Accessed: 2026-11-13
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Used in: Retrieval: giving the model evidence
Toolformer: Language Models Can Teach Themselves to Use Tools ↗
Timo Schick et al. · Published: 2023-02-09 · Accessed: 2026-11-13
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Used in: Tool use: letting a system act
WebGPT: Browser-assisted question-answering with human feedback ↗
Reiichiro Nakano et al. · Published: 2021-12-17 · Accessed: 2026-11-13
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Used in: Tool use: letting a system act
TruthfulQA: Measuring How Models Mimic Human Falsehoods ↗
Stephanie Lin, Jacob Hilton and Owain Evans · Published: 2021-09-08 · Accessed: 2026-11-13
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Holistic Evaluation of Language Models ↗
Percy Liang et al. · Published: 2022-11-16 · Accessed: 2026-11-13
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Red Teaming Language Models to Reduce Harms ↗
Deep Ganguli et al. · Published: 2022-08-23 · Accessed: 2026-11-13
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Extracting Training Data from Large Language Models ↗
Nicholas Carlini et al. · Published: 2020-12-14 · Accessed: 2026-11-13
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Deduplicating Training Data Mitigates Privacy Risks in Language Models ↗
Nikhil Kandpal, Eric Wallace and Colin Raffel · Published: 2022-02-14 · Accessed: 2026-11-13
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FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness ↗
Tri Dao et al. · Published: 2022-05-27 · Accessed: 2026-11-13
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Used in: Compute: the work behind the words
DeepSpeed Inference ↗
Reza Yazdani Aminabadi et al. · Published: 2022-06-30 · Accessed: 2026-11-13
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Used in: Compute: the work behind the words
Efficient Memory Management for Large Language Model Serving with PagedAttention ↗
Woosuk Kwon et al. · Published: 2023-09-12 · Accessed: 2026-11-13
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Used in: Latency: why a response takes time
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers ↗
Elias Frantar et al. · Published: 2022-10-31 · Accessed: 2026-11-13
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Used in: Quantization: fewer bits per number
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration ↗
Ji Lin et al. · Published: 2023-06-01; accessed revision 2026-04-25 · Accessed: 2026-11-13
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Used in: Quantization: fewer bits per number
Deep Dive into LLMs like ChatGPT ↗
Andrej Karpathy · Published: 2025 · Accessed: 2026-11-13
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[1hr Talk] Intro to Large Language Models ↗
Andrej Karpathy · Published: 2023 · Accessed: 2026-11-13
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Andrej Karpathy — official website ↗
Andrej Karpathy · Published: Undated; checked 2026-11-13 · Accessed: 2026-11-13
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