📖 Glosario

Referencias

Papers fundacionales

• Attention Is All You Need — Vaswani et al. (2017)
• Language Models are Unsupervised Multitask Learners (GPT-2) — Radford et al. (2019)
• Scaling Laws for Neural Language Models — Kaplan et al. (2020)
• Training Compute-Optimal Large Language Models (Chinchilla) — Hoffmann et al. (2022)

Modelos abiertos

• LLaMA: Open and Efficient Foundation Language Models — Meta (2023)
• LLaMA 2: Open Foundation and Fine-Tuned Chat Models — Meta (2023)
• The Llama 3 Herd of Models — Meta (2024)
• DeepSeek-V2: A Strong, Economical, and Efficient MoE — DeepSeek (2024)
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL — DeepSeek (2025)

Hardware e inferencia

• FlashAttention: Fast and Memory-Efficient Exact Attention — Dao et al. (2022)
• Efficient Memory Management for LLM Serving (vLLM) — Kwon et al. (2023)
• llama.cpp — Gerganov et al. (2023-2026)
• NVIDIA Ada GPU Architecture Whitepaper — NVIDIA (2022)
• H100 Tensor Core GPU Architecture — NVIDIA (2022)

Alineamiento y alucinaciones

• Training Language Models to Follow Instructions with Human Feedback (InstructGPT) — Ouyang et al. (2022)
• Constitutional AI — Anthropic (2023)
• GRPO: Group Relative Policy Optimization — DeepSeek (2025)