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Provides 5 million instruction–response pairs for supervised fine-tuning of code LLMs, with inputs, outputs, unit tests, and automated LLM judgments. Uses hybrid automated/synthetic generation and is released under CC BY 4.0 for large-scale SFT workflows.
Argues AI has entered its 'second half': a working recipe (language pre-training priors + scale + reasoning) now generalizes RL across tasks, so the bottleneck shifts from inventing methods to defining problems and rethinking evaluation.
Lets LLM agents drive real Android and iOS devices from natural-language commands by turning each screen's accessibility tree into structured text the model reads directly, not just screenshots. LLM-agnostic; runs via CLI, Python, or Docker.
Run large-language and multimodal models locally on edge devices (Android, iOS, desktop, web, Raspberry Pi) with hardware acceleration, function-calling, and multi-language SDKs—designed for low-latency, privacy-sensitive on-device inference.
Measures generative AI inference performance with token-level metrics (TTFT, inter-token latency), latency, and throughput under realistic traffic patterns. Provides a multiprocess engine, real-time TUI dashboard, extensible plugins, and integrations for telemetry and result uploads, aimed at inference benchmarking and capacity planning.
Provides a modular full-stack reinforcement learning stack to train and evaluate long-horizon, multi-turn tool-use LLM agents, including a performant trainer, a Tinker-compatible backend, agent orchestration, and Gymnasium-style environments for task design.
Archive of extracted and leaked system prompts behind major AI chatbots — Claude, ChatGPT, Gemini, Grok, Copilot, Perplexity and more — sorted by vendor and version with update dates, so you can read the hidden instructions and track how they change.
Converts PDFs into AI-ready structured outputs (Markdown, JSON with bounding boxes, HTML) for RAG and accessibility workflows; offers deterministic local parsing plus a hybrid AI mode for complex tables, OCR, formulas, and auto-tagging previews.
Transforms research papers, natural-language specs, and technical descriptions into runnable code via a multi-agent system. Covers Paper2Code, Text2Web, and Text2Backend; scores 75.9% on OpenAI's PaperBench, ahead of top ML PhDs.
A PyTorch DTensor-native SPMD library for training and fine-tuning LLMs, VLMs, diffusion and retrieval models. Integrates with Hugging Face for day-0 model support, provides YAML-driven recipes, DTensor/FSDP2 parallelism and NVIDIA-optimized kernels (Transformer Engine, DeepEP, FlexAttn).
Enables bidirectional checkpoint conversion between Hugging Face and Megatron formats and provides a PyTorch-native training library with tensor/pipeline parallelism, FP8/BF16 mixed precision, SFT and PEFT (LoRA) support for large and multimodal models.
Translates full-length books, subtitles, and documents with LLMs while preserving original formatting and structure. Uses intelligent chunking to handle arbitrarily long files, supports local or cloud providers, and resumes interrupted jobs without losing progress.