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AI Models 85003

• nor-100mb-after-wc-loglinear-newlex-before-ckpt500-packed-bfdiso_seed455↗

  francesca9805

• t2sql-gguf↗

  gguf-org

• Josiefied-Qwen2.5-1.5B-Instruct-abliterated-v1↗

  Goekdeniz-Guelmez

• byt5-small↗

  Google

  ByT5 is a tokenizer-free version of Google's T5 and generally follows the architecture of MT5. ByT5 was only pre-trained on mC4 excluding any supervised training with an average span-mask of 20 UTF-8 characters. Therefore, this model has to be fine-tuned before it is useable on a downstream task. ByT5 works especially well on noisy text data,*e.g.*, `google/byt5-small` significantly outperforms mt5-small on TweetQA.

• paligemma2-3b-pt-448↗

  Google

• t5-efficient-xl-nl16↗

  Google

• Phi-3-medium-128k-instruct-imatrix-GGUF↗

  hermes42

• tiny-random-starcoder2↗

  hyper-accel

• roberta-eus-euscrawl-base-cased↗

  ixa-ehu

• Qwopus3.6-27B-v2-MTP-GGUF↗

  Jackrong

  💡 What is Qwopus3.6-27B-v2-MTP? 🪐 Qwopus3.6-27B-v2-MTP is a speed-oriented reasoning release built on top of Qwen3.6-27B . It keeps the Qwopus line's focus on reconstructed reasoning traces, coding discipline, DevOps procedures, and mathematical derivations, while adding Multi-Token Prediction for faster generation. The goal is simple: preserve the depth and structure of a 27B reasoning model while making real interactive use noticeably faster. ⚡ MTP Decoding Auxiliary future-token prediction improves throughput on long reasoning, code, math, and strict-format prompts. 🧩 Structured Reasoning Inherits the Qwopus training recipe built around reconstructed step-by-step reasoning trajectories. 🧪 GB10 Tested Validated on a 30-question local benchmark across Logic, Coding, DevOps, Math, and Edge tasks. 🚀 Practical Speed Designed for workflows where strong answers matter, but waiting several extra minutes per task does not. Qwen3.6-27B provides the dense 27B foundation for this release. Qwopus3.6-27B-v2-MTP focuses on preserving the base model's broad reasoning capability while tuning the output style toward stepwise analysis, tool-aware execution, and practical engineering answers. Attribute Specifications & Details 🧠 Architecture Dense Transformer / 27 Billion Parameters 🎯 Focus Domains Agentic Coding, DevOps, structured logic, mathematics, and strict-format output ⚡ MTP Objective Improve generation throughput through multi-token speculative prediction while retaining final-answer quality.