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Fused CUDA kernels that compute exact attention without ever writing the full N×N score matrix to GPU memory, cutting memory from quadratic to linear and speeding up training and inference on A100/H100. Ships FlashAttention-2/3 plus KV-cache decode paths.
Transformer-based foundation model for tabular data that provides pre-trained checkpoints for fast classification and regression, with GPU-accelerated local inference and an optional cloud client. Best suited for small-to-medium datasets (~≤50k rows).
Runs pretrained diffusion models for image, video, and audio generation through composable pipelines. It separates pipelines, schedulers, models, adapters, and memory optimizations so teams can prototype quickly without locking into one model family.
Browser-based control panel for running Stable Diffusion locally, built on Gradio. Bundles txt2img, img2img, inpainting, outpainting, and upscalers (ESRGAN, GFPGAN, CodeFormer), plus an extension ecosystem and support for NVIDIA, AMD, and Intel GPUs.
Hands-on lecture series that teaches neural networks from first principles up to building a GPT: each lecture pairs a YouTube video with Jupyter notebooks and exercises so you code models (micrograd → MLPs → WaveNet-like convs → GPT) while learning training and debugging.
Unifies successive YOLO generations — YOLOv8, YOLO11, YOLOv3 and newer — under one package and a single `YOLO` API spanning detection, segmentation, classification, pose, oriented boxes and tracking, plus one-line export to ONNX, TensorRT and CoreML.
Multilingual sequence-to-sequence speech model and toolkit for speech recognition, speech-to-text translation, and language identification. Offers several model sizes (tiny → large/turbo) for different speed/accuracy trade-offs and ships with a CLI and Python API for offline transcription workflows.
Generate a lip-synced talking-head video from a single portrait image and an audio clip using learned 3D motion coefficients for realistic expression and head motion. Offers still/reference modes, Colab/HuggingFace demos, and an Apache-2.0 license.
Bundles ASR, voice activity detection, punctuation, and speaker diarization into one pipeline, with pretrained models like Paraformer and SenseVoice. SenseVoice runs ~17x realtime on CPU; also ships streaming ASR and an OpenAI-compatible API.
Web and desktop/mobile WebUI for generating, editing, captioning and processing images and videos with Stable Diffusion and many diffusion models. Key features include automatic model download, SDNQ on-the-fly quantization for VRAM savings, balanced CPU/GPU offload, multi-backend GPU support, and built-in captioning/tagging/upscaling workflows.
Reproduces GPT-2 (124M) from scratch on OpenWebText in ~4 days on an 8xA100 node, with the whole stack kept to two ~300-line files: train.py for the loop and model.py for the architecture. A char-level Shakespeare run finishes in ~3 minutes on one GPU.
X-AnyLabeling is a powerful annotation tool integrated with an AI engine for fast and automatic labeling. Designed for multi-modal data engineers, it offers industrial-grade solutions for complex tasks. Supports images and videos, GPU acceleration, custom models, one-click inference for all task images, and import/export formats like COCO, VOC, YOLO. Handles classification, detection, segmentation, captioning, rotation, tracking, estimation, OCR, VQA, grounding, etc., with various annotation styles including polygons, rectangles, rotated boxes.