Provides 500+ hours of human whole-body teleoperation recordings of a Unitree G1 in real homes, packaged in LeRobot v3.0 for robot learning. Contains 23K+ episodes, ~40M frames, multi-view 480p@30 video, 29-DoF states, actions and language annotations; CC BY 4.0 and large download size.
End-to-end pose-driven image-to-video model that animates a reference character from a driving video, supporting cross-identity replacement and multi-character scenarios without intermediate pose representations; performs best at 704p and ships as a diffusers-compatible checkpoint.
Provides a token-level benchmark for Russian PII detection and NER, with 2,841 sentences and 5,614 annotated spans across 21 fine-grained entity types in BIO format. Mixes sanitized production-log examples, synthetic document templates, and hard negatives to evaluate guardrails and anonymization pipelines.
Fine-tuned Hugging Face image-generation model that biases Ideogram-style prompts toward photorealistic outputs. Emphasizes natural lighting and realistic materials to reduce prompt tweaking; license not specified.
Provides a locally runnable, quantized GGUF release of Gemma 4 12B fine-tuned for Python coding with chain-of-thought distilled from Composer 2.5 and supplemented by Fable 5. Multiple quant options for low‑VRAM setups and execution‑verified training traces. Not safety‑aligned; validate before production.
Contains a sanitized Claude Code (Fable 5) JSONL transcript of a session that procedurally built a Boeing 747 in Three.js, including assistant messages, tool calls, and base64 screenshots — useful for studying agent trace, tool use, and vision self‑verification workflows.
Refines large-scale English pretraining corpora by predicting per-instance structured edits (insert, delete, replace) and deterministically applying them to produce cleaner text for LLM training. Provides five ~20B-token refined corpora in parquet with edit metadata and simple loading configs.
Provides GGUF quantized weights and runnable instructions to run CohereLabs' North-Mini-Code-1.0 (30B A3B MoE) locally via llama.cpp or vLLM; includes quant files, build/run notes, and recommended sampling and tool-use settings for agentic coding.
A community-distributed GGUF bundle of Google DeepMind’s DiffusionGemma (26B A4B) with multiple quantization variants for local image-text-to-text inference. Targets experimentation and offline deployment via the DiffusionGemma llama.cpp branch and llama-diffusion-cli; choose quantization for GPU memory vs. fidelity trade-offs.
Multilingual, low-latency text-to-speech model for speech generation and zero-shot voice cloning. Uses an MoE backbone with ECAPA-TDNN speaker embeddings, supports audio prefixes, fine-grained prosody/emotion controls and 44.1kHz output; optimized for Linux + NVIDIA GPUs.
A quantized 27B coder LLM fine-tuned for repository-level code generation, multi-turn tool calling, and agentic workflows — packaged for local GGUF/llama.cpp deployment with MTP speculative decoding and trace-inversion SFT. Optimized for developer tooling; experimental and not fully safety-validated.
Implements a blockwise sparse attention (MiniMax Sparse Attention) that scores and Top-k selects key-value blocks per Grouped Query Attention group to enable attention over million-token contexts. Paired with an exp-free Top-k GPU kernel and KV-outer sparse execution, it reduces per-token attention compute and yields large prefill/decoding speedups.