AI Model2026
Policy-adaptive multimodal safety classifier that evaluates text and images against free-form natural-language policies and returns a continuous yes/no safety score. Produces a single-token verdict from a 3B-parameter model, supports multiple languages, and is designed for lightweight real-time moderation.
AI Model2026
Provides GGUF-format fine-tuned Qwen3.6-27B weights optimized for consumer hardware, offering NEO IMATRIX and MTP quant variants, vision support, 256k native context, and uncensored 'heretic' traces with published benchmark improvements over the base model.
Embodied AI2026
Predicts eight future [x,y,yaw] waypoints for language-conditioned embodied person-following using fused DINOv3 and SigLIP visual features; trained with quality-driven, DAgger-style self-evolving data and optimized for on-device inference (~5+ FPS, ~180 ms).
AI Video Papers2026
Generates interactive long-horizon 24-fps video worlds (540p/720p) from text, image, or video inputs. Uses a 15B video diffusion transformer with a bounded visual context (sink frame, compressed temporal history, geometry-aligned spatial memory, recent-frame conditioning) and a discrete autoregressive distillation that cuts inference to ~4 sampling steps per chunk.
AI Model2026
Open preview checkpoint of a sparse Mixture-of-Experts causal LLM with ~314B total params (~13B active per token) and native 256K context for long-context multilingual text generation. Ships with custom modeling code (trust_remote_code) and a research/non-commercial use license.
AI Deploy2026
Shuo Yang, Xiaoze Fan +9University of California, Berkeley, Massachusetts Institute of Technology +2
Runs large open-weight MoE models locally by coordinating CPU and GPU resources, caching experts, and reallocating VRAM at runtime. It targets consumer hardware, from laptops to workstation GPUs.
AI Model2026
Generates and reasons about multimodal physical-world content—text, images, video and action trajectories—conditioned on text, images, video and robot/vehicle action inputs. An edge-sized (4B) Mixture‑of‑Transformers omni-model optimized for single‑GPU inference and Physical AI tasks (image→video, action prediction, robot policy).
AI Model2026
Provides GGUF-format quantized shards of Laguna S 2.1 for local or self-hosted inference—packaged for llama.cpp/llama-server and usable with vLLM/Transformers runtimes; targeted at long-context, agentic coding workloads.
AI Image2026
Performs instruction-based image editing from reference images using a 4B native-resolution diffusion transformer; the Turbo variant uses 4-step distillation for interactive latency (≈1.02 s per 1024² edit on A100) while supporting semantic, appearance, structure-aware and restoration edits.
AI Model2026
4-bit NVFP4 (W4A4) quantized pack of Upstage Solar Open2 250B for vLLM serving on NVIDIA Blackwell GPUs, preserving MoE routing and near-BF16 quality while cutting model size from 500.6 GB to 153.3 GB.
AI Model2026
Provides a 2‑bit quantized build of Qwen3.6‑35B‑A3B for local serving via an OpenAI‑compatible HTTP API. Key features: 12.3 GB on disk, eschamoe mixed 2/3‑bit expert quantization with int8 dense layers, runs on a single 16–24 GB NVIDIA GPU and ships with Escha SGLang and ZML runtimes.
Performs full-parameter post-training of trillion-parameter MoE DeepSeek-V4 models on an Ascend NPU SuperPOD, using a hierarchical optimization of model parallelism, communication orchestration, and kernel execution to increase Model FLOPs Utilization. Also builds CPT/SFT pipelines with solver-verified synthetic data for Operations Research, reporting strong zero-shot Pass@1 results.