AI Model2026
A pretrained autoregressive foundation LLM from Yandex that uses a hybrid MoE design (80B params, 3B active) and supports up to 262,144-token context. Notably strong on Russian factual knowledge, math and coding benchmarks; intended for research and fine-tuning rather than out-of-the-box production.
AI Audio2026
Encodes real audio into YuE2 semantic tokens and ships a matching NAR-branch LoRA so the YuE2 decoder renders realistic latents—enables tokenizing recordings, training artist LoRAs, and generating songs or covers. Includes an 8-layer tokenizer head and a rank-32 decoder LoRA; requires the YuE2 base models and GPU resources.
AI Model2026
Generates unified 768‑dimensional embeddings for text (including code), images, video and audio to enable cross‑modal semantic search and retrieval. Supports task instruction prefixes, Matryoshka truncation to 128/256/512/768 dims, and modular encoders for on‑device use under an Apache‑2.0 license.
AI Model2026
Unified text-to-image and prompt-based image editing model that natively outputs RGBA and accepts up to 10 reference images. Compact 7B visual generator optimized for 2K outputs and efficient inference; research-licensed under the Qwen Research License.
AI Model2026
Compresses a 27B-class multimodal model into end-to-end ternary weights to run 27B reasoning on-device: 5.9–8.6 GB deployed footprint, 262K-token context, ~98.2% of FP16 benchmark performance; ships MLX and GGUF packs and runs on Apple MLX and CUDA.
AI Dataset2026
Benchmark for typed probabilistic decisions: given one shared state, a model answers multiple typed questions at once and returns full probability distributions (noul/choice/score). Contains four workflows with train/test splits and soft gold labels from teacher samples, designed to evaluate accuracy, calibration, and latency trade-offs.
AI Model2026
Runs locally on constrained devices to turn text into guaranteed-parsable JSON tool calls, typed structured extractions, or sentence embeddings. Delivered as a single compact weights file (8–29 MB) with a laddered 2–20-layer design, low-bit quantisation and calibrated confidence scores for on-device apps.
AI Model2026
A 27B-class language model packaged in GGUF with end-to-end ternary weights for on-device or single-GPU llama.cpp inference; reduces FP16 footprint to ~5.9–7.2 GB while retaining ~98% of baseline performance and supporting up to 262K tokens.
AI Model2026
A 29B-parameter mixture-of-experts LLM for long-context reasoning and agent-style workflows, with native 256K context and Ascend NPU co-optimizations. Compatible with Transformers/vLLM ecosystems; suitable for long-horizon agents and domain fine-tuning.
AI Deploy2026
Evaluates multi-field JSON schemas in parallel to extract boolean or categorical field values from text, producing guaranteed-valid JSON and per-field calibrated confidences. Uses KV-cache broadcasting, sub-vocabulary logit slicing and token-tree disambiguation to cut latency (5.6x–7.0x on M4 Max) versus autoregressive decoding; requires Apple Silicon and MLX.
AI Model2026
Generates complete UI, poster and infographic layouts from text prompts, rendering legible in-image text and producing native RGBA output with transparent backgrounds. Suited for text-rich visual design workflows and pairs with a companion model that decomposes designs into editable layers.
AI Dataset2026
Provides a complete benchmark and training release for native audio‑visual dialogue: 2,800 synthesized audio‑visual dialogues, tiered rubrics, scoring code, ~28GB of 1080p media, and an RL reward recipe to evaluate and train omni models that take video+audio and return text.