AI Model2026
An open text-to-image generation model built on an 8B Diffusion Transformer that focuses on layout-sensitive, text-heavy, and instruction-following image synthesis. Notable for accurate text rendering, structured/compositional generation (posters, comics), and ability to run on consumer 24GB GPUs when paired with prompt enhancement.
AI Model2026
Provides a compact GGUF export of a tuned Gemma‑4 26B variant for local inference, optimized for llama.cpp and Apple Silicon to deliver faster, less‑censored chat and coding outputs. Includes Q4_K_M quantization and a neutral embedded template for more reliable local deployments.
AI Model2026
Multimodal agent model for long-horizon coding, image-text understanding, and autonomous task orchestration. Built as a 1T-parameter Mixture-of-Experts with 256K context and native int4 quantization — intended for coding-driven design, persistent background agents, and swarm-style sub-agent workflows.
AI Model2026
Open-weight multimodal 35B Qwen3.6 model in Hugging Face Transformers format that supports image/video/text inputs and native long contexts (262,144 tokens). Emphasizes agentic coding and preserved reasoning traces (thinking), uses an MoE-backed architecture and is designed for self-hosting with vLLM/SGLang/KTransformers; requires multi-GPU resources for production.
Embodied AI2026
Reconstructs camera poses and dense 3D point clouds from video streams using a feed‑forward foundation model. Combines a Geometric Context Transformer (anchor + local window + trajectory memory) with paged KV‑cache attention to enable stable, long‑sequence streaming inference (~20 FPS at 518×378).
AI Model2026
Performs feed‑forward streaming 3D reconstruction from image sequences, combining coordinate grounding, dense geometric cues and trajectory memory to correct long‑range drift; uses paged KV‑cache attention for ~20 FPS inference at 518×378 and supports sequences >10,000 frames.
AI Model2026
An uncensored, fully unlocked GGUF port of Qwen 3.6‑35B‑A3B for local multimodal (text+image) inference, offering K_P 'Perfect' quant variants (Q8–Q2) and an mmproj for vision. Suited for offline research and experimentation; not for use-cases requiring safety filters.
AI Model2026
Generates English text matching pre-1931 style — a 13B language model trained on ~260B tokens of pre-1931 English, useful for historical-language generation and stylistic research. An instruction-tuned variant exists for interactive tasks.
AI Model2026
Instruction-tuned 13B LLM post-trained on 260B tokens of pre-1931 English and finetuned with online DPO (LLM-as-judge) to improve instruction-following; suited for period-style English generation and etiquette/letter-writing formats, but not optimized for contemporary factual updates.
AI Model2026
An HDR LoRA fine-tune for Lightricks' LTX-2.3 (22B) that enables image‑conditioned any‑to‑any image-to-video and text-to-video generation. Designed for HDR-aware synthesis workflows; requires the LTX-2.3 base model and a LoRA-capable runtime.
AI Model2026
A 284B-parameter Mixture-of-Experts LLM with only 13B activated parameters, designed for 1,000,000-token contexts. Uses hybrid compressed attention and mixed FP4/FP8 precision to reduce long-context KV-cache and per-token FLOPs; aimed at long-document QA, RAG pipelines, and local/high-capacity inference.
AI Model2026
Generates conversational and reasoning outputs with support for million‑token contexts; uses a hybrid attention + MoE design to cut long‑context inference FLOPs and KV cache. Suited for long‑document retrieval, coding and complex reasoning; MIT licensed.