Runs one-command evaluation of vision-language models across 80+ multimodal benchmarks, handling data download, inference, and metric scoring in a single pass. Supports 220+ LMMs; adding a new model means writing one generate_inner() function.
Builds custom AI inference servers in pure Python on top of FastAPI, keeping full control over request logic while batching, GPU autoscaling, streaming, and OpenAI-spec endpoints come built in. Claims a 2x+ throughput edge over plain FastAPI.
Triton kernels and PyTorch layers for linear-attention, state-space, and sparse-attention token mixers (GLA, RWKV, Mamba2, GSA) as drop-in replacements for multihead attention. Runs on NVIDIA, AMD, and Intel GPUs with Hugging Face support.
Traces how Transformer LLMs route information from input to output, attributing each block's effect to individual attention heads and feed-forward neurons. Click any edge to see what a head promotes or suppresses in vocabulary space.
Serves large language and multimodal models with low latency and high throughput using RadixAttention, continuous batching, structured outputs, parallelism, quantization, and broad accelerator support.
Clones a voice from a 5-second sample for zero-shot TTS, or fine-tunes on ~1 minute of audio for few-shot synthesis. Covers Chinese, English, Japanese, Korean, and Cantonese, with a WebUI bundling vocal separation, ASR, and dataset labeling.
Converts e-books (epub, pdf, mobi, docx, and more) into chapter-aware audiobooks, with optional zero-shot voice cloning. Bundles eight TTS engines including XTTSv2 and Bark, and covers 1,158 languages via Meta's MMS — all runnable on CPU or GPU.
A family of GUI agents that operate phones, desktops, and browsers by perceiving the screen visually rather than reading app code. Ships open GUI-Owl vision-language models (7B/32B) plus a multi-agent framework for planning, reflection, and tool use.
A PyTorch-native, hardware-agnostic stack for robot learning: data collection, training, and deployment across 11+ robots, from SO100 to Unitree G1. Includes imitation, RL, and vision-language-action policies (ACT, Diffusion, Pi0, SmolVLA).
Pocket-sized multimodal LLM for efficient image- and video-understanding on mobile and edge devices, featuring mixed 4x/16x visual-token compression (MiniCPM‑V 4.6), compact 1.3B variants, and ready guides for iOS/Android/HarmonyOS deployment.
Creates personalized digital avatars (AI twins) by fine-tuning LLMs on users' chat history and binding them to chatbots. Provides an end-to-end pipeline — chat export, preprocessing with privacy filters, SFT/LoRA training, and deployment (Telegram/Discord/Slack). Best with larger models and substantial chat data.
Runs GPT-4o-class vision, speech, and full-duplex audio-video conversation on a 9B model small enough to deploy on phones and tablets. The 4.5 release scores 77.6 on OpenCompass and adds real-time bilingual voice with voice cloning.