AI Model2026
A 2B-class causal LLM packaged as a GGUF for local inference; offers 131072-token native long context, XML-style tool-calling support, and is tuned with SFT + RL + OPD using the UltraData family for stronger code, math and agentic abilities.
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 Dataset2026
Provides ~86K verifiable-reward RL training samples across Math, Knowledge (STEM), Long-Context, and Code for post-training LLMs; each sample includes a ground-truth and verifier-friendly JSONL format for stable reward signals. Note: code tasks require an external sandbox to execute tests.
AI Model2026
Provides per-tensor mixed-precision GGUF quantizations of the Qwen3.8-Flash-Next MoE model using GSQ (Gumbel-Softmax Quantization) to produce tensor-level quantized variants and RCO (Riemannian Constrained Optimization) to allocate quant types under exact size budgets; includes a BF16 vision projector and three size/bit-width operating points.
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
Handles text and speech input, producing text, speech, or both for streaming voice interaction, multilingual audio tasks, and spoken tool calls. Its 2B-parameter size targets local and edge use, not complex multi-step reasoning.
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 Dataset2026
Selected ~245B-token corpus of algorithmically relevant code across 11 languages from The Stack v3, produced by file-level language-adaptive selection (UltraData-Code-L2-Classifier). Concentrates problem-solving implementations for LLM code pretraining while preserving broad language coverage; released under ODC-By.
AI Model2026
Multimodal agentic model for long-horizon computer and browser tasks, with visual self-correction and function-calling. The Pro variant is a 397B Mixture-of-Experts (≈17B active) model with a 262,144-token context window, Docker deployment recipes, and weights currently marked “coming soon.”
Adds a Next Concept Prediction objective that predicts discrete, product-quantized latent concepts spanning multiple tokens alongside next-token prediction, yielding faster pretraining convergence and measurable downstream gains with an 8.9B-parameter latent-space LLM.