AI Model2026
27B multimodal LLM post-trained to prioritize agentic, weight-scaled reasoning over 64K-token contexts. Built on Qwen3.6-27B and released with BF16 weights plus several GGUF quants; aimed at coding, long-document reasoning, tool use and multimodal inspection.
AI Video2026
Generates videos from text and image+text prompts using a 30B Mixture-of-Experts model tuned for embodied intelligence; includes a refiner and structured prompt rewriter, and supports diffusers/SGLang runtimes with multi-GPU inference.
AI Model2026
A GGUF-distributed Qwen3.6 35B MoE model variant repaired with a
AI Model2026
GGUF conversions of Laguna S 2.1 for llama.cpp, including quantized builds (Q4_K_M, Q8_0, F16) and a small DFlash drafter for speculative decoding; configured for a 256K default context window and intended for local inference and serving with Poolside's llama.cpp fork.
AI Model2026
End-to-end 0.8B multimodal OCR and page-level document parser that converts page images into structured Markdown (text, LaTeX formulas, HTML tables, and image crops). Post-trained from Qwen3.5-0.8B using mixed real/synthetic data and SFT+RL+OPD; achieves 96.58 on OmniDocBench v1.6.
AI Model2026
A GGUF-local variant of Qwen3.6-35B that applies a non-training 'Genesis' tensor-repair process and Hermes-agent fine-tuning to enable uncensored, multimodal (text+image) local inference. Highlights: MoE 35B spec, large native context, Hermes function-calling dataset transfer, and recommended quantization/runtime settings.
AI Model2026
Provides low-bit quantized Hy3 (hy_v3) GGUF model weights and mixed-precision quantization recipes for running Hy3 on llama.cpp, with optional MTP self-speculative decoding and imatrix-based calibration for improved quality/speed trade-offs.
AI Model2026
Agentic coding and long-horizon text generation via a 118B-parameter Mixture-of-Experts LLM with a 1,048,576-token context window. Features 256 routed experts, native preserved-thinking (reasoning) control, speculative decoding draft models, and quantized checkpoints for lower-cost serving.
AI Model2026
Generates 2048-d multilingual text embeddings for retrieval and semantic search, suited for RAG and dense retrieval. Pruned and distilled from the Ministral-3 family into a ~1.14B BF16 model, supports long contexts (up to 32,768 tokens) and optimized for NVIDIA GPU inference.
AI Model2026
Timestamp-aware realtime video→text model that processes incoming frames continuously, answers questions mid-stream or emits silence when evidence is insufficient, and can revise earlier outputs as new frames arrive. Built for timestamped multimodal interaction with a 256K context and an 11B-parameter backbone.
AI Model2026
Accepts text, image and audio inputs and generates text outputs for conversational, instruction-following and multimodal tasks; a sparse-MoE autoregressive model (975B total, 41B active) with BF16/NVFP4 support and local-deploy recipes.
Enables RL post-training with million-token prompts under a fixed GPU budget by evaluating shared prompt state without autograd, retaining only minimal model state, and replaying short response branches; instantiated as GRPO and demonstrated on Qwen3.6-27B and GLM-5.2 up to multi-million token execution.