Provides leaderboard-ready test splits for the Open ASR Leaderboard: converts unsafe custom loaders to Parquet, sorts samples by audio length, and packages eight ESB test sets (LibriSpeech, Common Voice, GigaSpeech, SPGISpeech, etc.) for reproducible ASR benchmarking.
Provides code, pretrained weights, and tooling for protein language models and structure prediction — including ESMC, ESMFold2, sparse autoencoders (SAEs), and the ESM Atlas. Includes model checkpoints, tutorials, Hugging Face & Biohub integration, and an MIT license.
A research codebase and model family for vision–language models that experiments with data‑centric post‑training strategies and long‑context multimodal reasoning. Includes model reports, released research weights (non‑commercial), grounding tools (LocateAnything) and integrations for inference/optimization.
aisuite is a lightweight Python library that provides a unified API for working with multiple Generative AI providers. It supports models from OpenAI, Anthropic, Google, Hugging Face, AWS, Cohere, Mistral, Ollama, and others—abstracting away SDK differences, authentication details, and parameter variations. Modeled after OpenAI’s API style, it enables developers to build LLM-based or agentic applications across providers with minimal setup.
Open-source TTS that clones a voice from 3-10s of audio and synthesizes cross-lingual speech in 9 languages and 18+ Chinese dialects. Supports streaming at ~150ms latency with instruction control over emotion, speed, and accent.
Turns a UI screenshot into structured elements so a vision LLM can act without HTML or accessibility trees. A fine-tuned detector finds interactable icons; a caption model describes their function, lifting GPT-4V grounding on ScreenSpot and Mind2Web.
Official inference framework for 1-bit and ternary (1.58-bit) LLMs such as BitNet b1.58, with optimized CPU kernels. Delivers 1.37x-6.17x speedups and 55-82% lower energy on x86 and ARM, and runs a 100B model on a single CPU at 5-7 tokens/sec.
Chains four swappable open modules — voice activity detection, speech-to-text, an LLM, and text-to-speech — into a local voice agent that needs no proprietary APIs. Runs on CUDA, Apple Silicon, or Docker, with an OpenAI-compatible realtime WebSocket mode.
Runs local LLM, vision-language, ASR, OCR, and image-generation models across NPU, GPU, and CPU from one command. Differs from Ollama and llama.cpp with first-class Qualcomm Hexagon NPU support and day-0 coverage of new models like Qwen3-VL.
Trains a 65M-parameter vision-language model from scratch in ~2 hours on one RTX 3090, about 3 RMB (~$0.40) of GPU rental. Connects a frozen SigLIP2 encoder to a small MiniMind LLM via a two-layer MLP projector; full PyTorch code for pretraining and SFT.
Turns PDFs and images into clean Markdown with a 7B vision-language model, keeping tables, equations, handwriting, and multi-column reading order while removing headers and footers. Runs on one 12GB+ GPU at about 1/32 the cost of GPT-4o APIs.