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 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 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 Train2023
Streamlines post-training and fine-tuning for large language and multimodal models with a single YAML-driven pipeline. Supports LoRA/QLoRA, full fine-tuning, preference tuning, RL methods, multi-GPU/FSDP/DeepSpeed, and many model backends (Hugging Face, local checkpoints).
AI Train2023
Connects a frozen vision encoder to a language model via visual instruction tuning, yielding an open multimodal assistant that follows image-grounded instructions. Released checkpoints span 7B-34B and approach GPT-4V on vision-language benchmarks.
AI Dataset2023
Contains short, small-vocabulary stories synthetically generated by GPT-3.5 and GPT-4 for training and evaluating compact language models. Includes multiple splits, a GPT-4-only V2 subset, and archive files with prompts and metadata for reproducible experiments.
AI Train2023
Fine-tunes 100+ LLMs and VLMs from one config file or a no-code web UI, unifying LoRA, QLoRA, full tuning, DPO, PPO, KTO and ORPO behind a single interface. Bundles GaLore, Unsloth, FlashAttention-2 and 2-8bit quantization to fit a single 24GB GPU.
Teaches generative AI app development through 21 lessons covering LLM basics, prompting, chat, search, image generation, agents, RAG, fine-tuning, small models, and responsible AI.