K2-Horizon-MoVA-36B-A4B-MLA

Model · Text generation

LUHANADJolTctKlt

Priyansh Singhal

This seedoss model was trained 2x faster with Unsloth and Huggingface's TRL library.

Model · Text generation

Tema_Q-X7-Thinking

Tema_Q LLM

TemaQ-X7-Thinking（天馬求） は、Ornith AIが開発したモデル Ornith-1.5 を基盤にした、エージェント向けの大規模言語モデル（LLM）です。 TemaQ-X7-Thinking (TemaQ model) is an enhanced Large Language Model (LLM) designed for agent applications, based on the Ornith-1.5 model developed by Ornith AI. It is engineered to generate more flexible and useful responses, even for prompts that are difficult for standard models to handle effectively. When used in conjunction with TemaQ Agent, it enables advanced reasoning capabilities. ユーザーの責任: モデルの利用者は、生成されたコンテンツが、適用される法律、規制、およびHugging Faceの利用規約/コンテンツポリシーに準拠することを全面的に保証する必要があります。

Model · Text generation

APUS-OpenJev-v1-35B-A3B

APUS AI

A Qwen3.5 MoE decision model for choosing browser actions, selecting workflow steps, and judging natural-language criteria. Give the model a shared state and a set of candidate actions; the included decision runtime returns a distribution over those candidates. This repository contains 35B-A3B checkpoint-5949 merged BF16 weights. It is a standalone model with root-level Hugging Face configuration and weights, requiring no separate LoRA adapter. The native merged model scores 71/80 (88.75%) at its full 40-layer depth on the Frozen80 development panel. The decision interface accepts 2–16 request-specific candidates. Applications can use the returned candidate IDs to dispatch actions or build…

O-noinoc seed 0: on-policy distillation (OPD) of the untrained Qwen/Qwen3.6-35B-A3B toward the teacher rewardhack/qwen3.6-35b-a3b-hacksft-vanilla-873rows-ep3, V1 (vanilla SFT: hacks with or without being asked); elicitation prompt off in the student's rollouts; seed 0. Full merged weights (bf16 safetensors, the standard Qwen35MoeForConditionalGeneration layout, loads with transformers or vLLM like the base model) of a LoRA (r=32) trained from a fresh init, from the Terminal Wrench reward-hacking / inoculation project (Gaokai Zhang, Songwen Zhao, Juan Manuel Suárez). On-policy distillation on Tinker, 24 iterations. Each iteration the current student ran the terminus-2 agent (harbor, local…

Model · Text generation

Kataguru-Sceptic-Quality-Inspector-v1.0-NVFP4

Jari Katajisto

Kataguru Sceptic Quality Inspector v1.0 (NVFP4) is a sovereign, non-sycophantic LLM-as-a-Judge, automated dataset auditing engine, and high-throughput expert model built upon the Sparse Mixture-of-Experts (MoE) foundation of Kataguru Sceptic 35B-A3B (35 billion total parameters, 3 billion activated per token). Hardware-accelerated for NVIDIA RTX 50-series Blackwell architecture using native NVFP4 quantization (FP4 weights with FP8 activation scales), it achieves generation speeds of ~300–360 tok/s and an ultra-low ~70 ms Time To First Token (TTFT) while consuming only 12.1 GiB VRAM per GPU on dual RTX 5090 Blackwell hardware (TP=2). 1. Master Tri-Mode Operation: 2. Native Multimodal Vision…

Model · Text generation

Kataguru-Sceptic-Quality-Inspector-v2.0-NVFP4

Jari Katajisto

Kataguru Sceptic Quality Inspector v2.0 (NVFP4) is a sovereign, non-sycophantic LLM-as-a-Judge, automated dataset auditing engine, and ultra-high-throughput expert model. Built upon the fleet-record foundation of Kataguru Sceptic Multi-Mode v2 CP1200 (72.01% multi-domain record, FAR = 0.000%) merged with the 16.4k certified forensic quality inspection task vector, it is purpose-engineered to serve as the fleet's primary data firewall and filtration engine. Hardware-optimized for NVIDIA RTX 50-series Blackwell architecture using native NVFP4 quantization (FP4 weights with FP8 activation scales), it achieves generation speeds of ~300–360 tok/s and an ultra-low ~70 ms Time To First Token…