AI Model2026
Post‑trained 9B causal language model optimized for agentic workflows, tool use, coding, and long‑context instruction following. Uses a routing‑guided agentic post‑training pipeline that converts harness executions into training signal, improving agentic and coding benchmarks. Text‑only weights (safetensors/BF16), Apache‑2.0.
AI Model2026
A 4B causal language model post‑trained from Qwen3.5‑4B for agentic workloads — tool use, coding and instruction following — using a routing‑harness feedback loop aimed at iterative capability improvement; distributed as text-only safetensors with native 262,144-token context.
AI Model2026
Compact causal LLM for on-device assistants, coding agents and long-context tool use — ~2.52B parameters with a 131,072-token context, trained with SFT + RL + OPD and released with its UltraData training corpora and multi-format deployment checkpoints.
AI Video2026
LoRA adapters for MiniMax H3 that sharpen and enhance videos in ComfyUI by conditioning on source clips via guide latents for pixel-level alignment. Designed mainly for ref2va as a second-pass sharpening tool, includes a ComfyUI workflow and example before/after clips; requires aligned guide clips at the target resolution and valid clip lengths.
AI Model2026
A multimodal, agentic LLM optimized for long‑horizon, visually grounded workflows — capable of operating browsers and terminals and autonomously executing and testing code. Open‑source weights are available and the family ships in mini, Pro and Max variants for different compute/quality tradeoffs.
AI Model2026
Runs a 35B-class sparse MoE LLM with SSD-streamed experts, 4-bit quantization, prerouter routing prediction and Recover-LoRA adapters to enable ~2.9–3 GiB active memory and interactive decoding (~15 tok/s) for on-device inference.
AI Model2026
A reasoning‑efficient fine-tune of Qwen3.8-27B that penalizes overthinking tokens to shorten internal reasoning traces — about 58.3% fewer thinking tokens with <1% accuracy loss and ~1.95× speedup; designed for long-context, multimodal and quantized deployments.
AI Audio2026
Ruibin Yuan, Hanfeng Lin +56Multimodal Art Projection (MAP), Hong Kong University of Science and Technology (HKUST)
Generates full songs from lyrics and a style prompt, producing vocals and accompaniment and exporting editable symbolic scores for melody/chord control. Uses symbolic planning with agentic multi-turn editing and runs local 48 kHz inference on a 24GB GPU.
AI Audio2026
Provides low-latency, true-streaming automatic speech recognition that emits append-only committed transcripts to avoid partial-result rollbacks. Supports configurable decoding chunks (80 ms–2 s), optimized for Chinese and English, and offers vLLM and transformers backends for real-time deployment.
AI Model2026
A multimodal Mixture-of-Experts foundation model with a million-token context window; uses a causal encoder–decoder layout and aggressive KV-cache compression (~890 bytes/token) to limit per-token activation to 8B/16B—designed for long-context, agentic, and multimodal workloads.
AI Model2026
Provides a drop-in checkpoint of DeepSeek-V4.1-Flash with weight-level abliteration that removes safety guardrails to produce uncensored outputs; preserves vision, MoE routing, 1M-token context and native FP8 quantization. Intended for advanced self-hosted deployment; requires large NVLink GPU domains and careful serving setup.
AI Model2026
Rewrites AI-generated English and Chinese drafts so they read like human writing while preserving every number, date, unit, name and quote. Runs locally with multiple GGUF quantized builds and a strict byte-for-byte prompt format for consistent rewrites.