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.
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.
AI Infra2023
Runs 70B-class LLM inference on a single 4GB GPU without quantization and supports Llama3.1 405B on 8GB VRAM. Uses layer-splitting and block-wise model compression (4/8-bit) to reduce disk load and can speed up inference loading by up to ~3x; integrates with Hugging Face models.
AI Deploy2023
Run any open-source LLM, embedding, speech, image, or multimodal model behind one OpenAI-compatible API — swap GPT for an open model in a single line. Routes across vLLM, llama.cpp, GGML, and TensorRT, scaling from a laptop to a multi-node GPU cluster.