AI Dataset2022
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.
AI Dataset2022
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.
AI Image2022
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.
AI Train2022
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.
AI Deploy2023
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.
AI Dataset2023
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.
AI Infra2023
Runs large language models entirely in C/C++ with no external dependencies, using 1.5-to-8-bit integer quantization and CPU+GPU hybrid inference to fit models larger than available VRAM. Backs Ollama, LM Studio, and most local-inference tooling.
AI Dataset2023
Provides 52,000 English instruction–response pairs generated by OpenAI's text-davinci-003 for instruction-tuning language models. Released under CC BY-NC 4.0; low-cost synthetic data useful for research but contains model-generated biases and errors.
AI Infra2023
Puts OpenAI-, Anthropic- and Ollama-compatible endpoints in front of 60+ inference backends, so existing client code runs unchanged against local models for text, vision, audio, image and embeddings. Runs CPU-only or accelerated, data stays local.
AI Audio2023
Web UI to train and run retrieval-based voice conversion models from small datasets (≈10 minutes), featuring top-1 feature retrieval to avoid timbre leakage, model fusion, real-time conversion, vocal separation, and multi-hardware support.
AI Dataset2023
A 15,000+ English instruction–response corpus for fine-tuning and evaluating LLM instruction-following behavior. Contains human-authored prompts and answers across categories (closed/open QA, summarization, extraction, classification, brainstorming) and is released under CC BY-SA 3.0.