AI Dataset2026
Provides 324 Russian short-answer web-search tasks with gold supporting documents to evaluate fixed-index retrievers and search agents. Tasks span eight topical categories and five retrieval challenge types (multihop, structured evidence, temporal, entity disambiguation, comparative) and use a Qwen3-Embedding-8B index for evaluation.
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.
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.
AI Video Papers2026
Predicts variable-cardinality sets of evidence intervals in videos to temporally ground queries using multimodal large language models. Combines caption-derived multi-span supervision, a temporal Wasserstein matching-free reward, and temporal IoU, yielding strong mIoU gains across multiple benchmarks.
AI Model2026
GGUF-quantized releases (NEO IMATRIX + MTP) of a multi-stage fine-tuned, uncensored Qwen3.5-9B model with vision enabled and a native 256k context window—optimized for instruction following, reasoning and image-text-to-text workflows; released under Apache-2.0.
AI Model2026
Efficient 4B native-resolution diffusion foundation model for text-to-image generation and instruction-based image editing. Uses a lightweight Mage‑VAE tokenizer and a 4B NR‑MMDiT backbone to produce 512–2048 outputs with low memory and fast inference; ships in Base, RL-aligned and few-step Turbo variants.
AI Dataset2026
A curated collection of 49,772 teacher-generated chat traces from qwen3.8-max-preview for supervised fine-tuning and off-policy distillation. Preserves visible chain-of-thought blocks, emphasizes math/code/reasoning mixes, and includes provenance and licensing cautions tied to Alibaba Cloud Model Studio.
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.
AI Model2026
An open-weight LLM checkpoint post-trained for agentic deep search: Qwen-compatible reasoning and tool-call formats optimized for web browsing, multi-source evidence aggregation, long-horizon planning and recovery from failed interactions; typically paired with the AxisAgentic harness.
AI Model2026
An open-weight, Qwen-derived thinking model optimized for agentic deep web search and long-horizon planning. Provides Qwen-compatible reasoning and tool-call formats for English/Chinese browsing, multi-source evidence aggregation, source verification, and recovery from failed environment interactions.
Converts image-content removal into a contrastive on-policy self-distillation signal: the EMA teacher produces next-token distributions with and without image content, uses their log-probability differences to sharpen visual-grounded candidates, and distills that full-distribution target into the student—no external teacher or extra inference cost.
AI Model2026
A text-only open-weight MOE code model (35B total, 3B active) fine-tuned with SFT+RL for agentic coding; achieves strong agentic-code benchmarks, supports 262k context and deployment via Transformers/vLLM; vision weights are not included.