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Open-source framework that accelerates fine-tuning and full training of transformer LLMs by up to 30 × while cutting VRAM requirements by roughly 90 %, letting developers train custom models quickly on commodity GPUs.

A lightweight open-source platform for running, managing, and integrating large language models locally via a simple CLI and REST API.

End-to-end NVIDIA framework and micro-services platform for building, customizing, and deploying large language, speech, vision, and multimodal AI models.

The paper “Attention Is All You Need” (2017) introduced the Transformer — a novel neural architecture relying solely on self-attention, removing recurrence and convolutions. It revolutionized machine translation by dramatically improving training speed and translation quality (e.g., achieving 28.4 BLEU on English-German tasks), setting new state-of-the-art benchmarks. Its modular, parallelizable design opened the door to large-scale pretraining and fine-tuning, ultimately laying the foundation for modern large language models like BERT and GPT. This paper reshaped the landscape of NLP and deep learning, making attention-based models the dominant paradigm across many tasks.

This paper introduces a Relational Memory Core that embeds multi-head dot-product attention into recurrent memory to enable explicit relational reasoning. Evaluated on synthetic distance-sorting, program execution, partially-observable reinforcement learning and large-scale language-modeling benchmarks, it consistently outperforms LSTM and memory-augmented baselines, setting state-of-the-art results on WikiText-103, Project Gutenberg and GigaWord. By letting memories interact rather than merely store information, the approach substantially boosts sequential relational reasoning and downstream task performance.

This paper introduces GPT-2, showing that large-scale language models trained on diverse internet text can perform a wide range of natural language tasks in a zero-shot setting — without any task-specific training. By scaling up to 1.5 billion parameters and training on WebText, GPT-2 achieves state-of-the-art or competitive results on benchmarks like language modeling, reading comprehension, and question answering. Its impact has been profound, pioneering the trend toward general-purpose, unsupervised language models and paving the way for today’s foundation models in AI.

Open-source, node-based workflow-automation platform for designing and running complex integrations and AI-powered flows.

reveals that language model performance improves predictably as you scale up model size, dataset size, and compute, following smooth power-law relationships. It shows that larger models are more sample-efficient, and optimally efficient training uses very large models on moderate data, stopping well before convergence. The work provided foundational insights that influenced the development of massive models like GPT-3 and beyond, shaping how the AI community understands trade-offs between size, data, and compute in building ever-stronger models.

This paper introduces GPT-3, a 175-billion-parameter autoregressive language model that achieves impressive zero-shot, one-shot, and few-shot performance across diverse NLP tasks without task-specific fine-tuning. Its scale allows it to generalize from natural language prompts, rivaling or surpassing prior state-of-the-art models that require fine-tuning. The paper’s impact is profound: it demonstrated the power of scaling laws, reshaped research on few-shot learning, and sparked widespread adoption of large-scale language models, influencing advancements in AI applications, ethical debates, and commercial deployments globally.

This tutorial offers a detailed, line-by-line PyTorch implementation of the Transformer model introduced in "Attention Is All You Need." It elucidates the model's architecture—comprising encoder-decoder structures with multi-head self-attention and feed-forward layers—enhancing understanding through annotated code and explanations. This resource serves as both an educational tool and a practical guide for implementing and comprehending Transformer-based models.

Open-source framework that provides composable building blocks to create, orchestrate and monitor LLM-powered applications and agents.

Data framework that connects large-language models to private or enterprise data via indexing, retrieval and agent orchestration.