Runs text-to-video, image-to-video, text-to-image, and image editing inference with acceleration, offloading, quantization, and distributed execution for large visual generation models.
Packages Hugging Face ML tasks—dataset creation, model training, evaluation, Hub ops, Spaces deployment—as portable Agent Skills. Each is a SKILL.md folder agents load on demand, running unchanged across Claude Code, Codex, Gemini CLI, and Cursor.
Converts images (and other conditions) into high-fidelity, fully textured 3D assets using a 4B-parameter generative model and a field‑free sparse voxel format (O‑Voxel). Handles arbitrary topology, PBR materials, and near real-time mesh/voxel conversions; requires Linux and an NVIDIA GPU with >=24GB memory.
Collects ~200,000 human responses to 20 visual/semantic association questions (e.g., Bouba–Kiki), with per-response image options and demographic metadata — useful for cross‑cultural perception and evaluation of multimodal systems, but not guaranteed as a rigorously controlled experimental sample.
Detects and surgically removes Google's SynthID watermark from images using multi-resolution spectral analysis and a resolution-aware codebook; provides a V3 bypass with high PSNR and strong phase-coherence reduction. Research-focused and intended for analysis/defense, not misrepresentation.
Large-scale, real-world dual-arm video corpus for embodied robotics and reinforcement-learning research — over 1TB of multimodal recordings on Hugging Face, intended for training and evaluating agents in real manipulation scenarios; CC BY‑SA 4.0.
Enables parallel speculative decoding by using a lightweight block-diffusion draft model to produce multi-token drafts for faster, high-quality generation. Integrates with vLLM, SGLang and Transformers backends and ships draft models on Hugging Face.
Real‑time full‑duplex speech‑to‑speech system that controls conversational role via text prompts and voice timbre via audio-conditioned embeddings. Built on Moshi; optimized for low-latency, persona-consistent spoken interactions.
Generates low-latency, streaming text-to-speech entirely on CPUs (no GPU or cloud API required), using an ~100M-parameter model with voice cloning and multilingual support. Optimized for low resource use (2 CPU cores, ~200ms to first audio chunk) — suited for local, privacy-sensitive, or embedded TTS.
Provides open ASR and TTS speech data for 24 Sub‑Saharan African languages to train and evaluate speech models. Includes ~1,250 hours of transcribed ASR and ~235 hours of single‑speaker TTS with train/validation/test/unlabeled splits and mixed CC-BY licenses.
Large-scale mathematical reasoning dataset of model-generated solution trajectories produced with and without Python Tool-Integrated Reasoning (TIR), with final answers verified against reference solutions. Contains ~3.64M JSONL training samples (~144 GB) and per-source CC-BY / CC-BY-SA licensing; intended for training and evaluating tool-augmented mathematical reasoning in LLMs.
Processed, multilingual news corpus of 1.357B articles extracted from Common Crawl CC‑News with per-article WARC provenance. Includes trafilatura-extracted bodies, language labels (GlotLID & CommonLingua), IPTC topic tags, monthly Parquet shards and a companion FM-index for sub-10ms substring queries; bulk text access is gated for academic research.