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.
Contains 40,000 teacher-generated reasoning traces distilled from the Qwen3.8-27B model for supervised fine-tuning and analysis. Covers code, math, science and logic; each example pairs a <think> chain-of-thought with a final response and is distributed in JSONL/Parquet for SFT workflows.
Rust-and-Python toolkit that serves open-source LLMs (Llama, Falcon, Mixtral, StarCoder) over HTTP/gRPC with tensor parallelism, continuous batching, Flash/Paged Attention and quantization. Now in maintenance mode, pointing users toward vLLM and SGLang.
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.
Curated, community-maintained collection of ready-to-use prompts — roles like interviewer, translator, and code reviewer — that copy directly into ChatGPT, Claude, Gemini, or local models. Available as CSV, markdown, and a Hugging Face dataset.
Provides human preference comparison pairs and red-team conversation transcripts collected by Anthropic for training preference/reward models and studying harmful model behaviors; intended for RLHF and safety research, not for supervised fine-tuning of dialogue agents.
Community-curated collection of ChatGPT-style prompts mirrored as a Hugging Face dataset; organized by task and model compatibility for quick reuse. Useful for prompt engineering, text-generation prototyping, and building conversational examples across multiple LLMs.
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.
Open-source LLM inference and serving engine built around PagedAttention, which manages the KV cache like OS virtual memory to cut waste and raise throughput. Supports continuous batching, KV cache sharing, quantization, and an OpenAI-compatible API.
Contains tech-blog posts scraped from Habr (primarily Russian, some English) in Parquet format with ~100K–1M records. Suited for multilingual text-generation and language-model fine-tuning; license is not specified, so verify before redistribution.