AI Model2026
27B multimodal reasoning model built on Qwen3.5-27B that preserves the base model's native multi-token-prediction head, full vision tower, and a 1,048,576-token YaRN context window. Designed for agentic tool use, long-context reasoning, and research deployments; released under Apache-2.0.
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.
Uses pretrained multimodal LLMs as zero-shot, training-free reward models for text-to-image RL by scoring how well the original text prompt can be recovered from a generated image via image-conditioned prompt log-likelihood; includes a Self-SpectraReward closed-loop variant.
AI Agent Papers2026
Acquires repository knowledge via a targeted QA loop before generating patches, decoupling knowledge acquisition from repair. A Questioner and Answerer produce evidence-grounded QA pairs that a Resolver uses to generate fixes; improves Pass@1 on SWE-bench Verified with modest overhead.
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
GGUF-quantized builds of a 1B 'Thinking' MiniCPM5 model fine-tuned on Fable 5 (V2) for local runtimes; enhances tool/function-calling, coding and instruction-following, and supports long contexts (up to 128K tokens).
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.
Builds structured knowledge graphs for retrieval-augmented generation via a multi-step GraphRAG pipeline that separates extraction from consolidation. Key features include typed two-stage extraction, DBSCAN-backed deduplication, LLM summarization, Leiden community detection, and a compact 7B extractor model (Meno-Lite-0.1).
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 Dataset2026
50,000 distilled conversational traces (≈120M tokens) generated from GLM-5.2 for high-reasoning text generation and QA, covering STEM, programming, creative and support dialogues; Apache-2.0 licensed.
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 Dataset2026
Provides behavior-preserving next-step training traces from Claude Fable 5 for supervised fine-tuning and analysis of instruction-following, tool-calling, and coding agents. Runtime-normalized, independently verified, and supplied as Parquet/JSONL with 13,357 cumulative rows from 2,443 accepted trajectories.