Teutonic-I-Reliquary-SFT

Model · Text generation

dQwen3.5-9B-Base

IFML

A masked diffusion language model adapted from Qwen3.5-9B. The backbone is hybrid: only its attention layers are made bidirectional, and the Gated DeltaNet layers stay causal. This is a base model, with no instruction tuning. Paper: dQwen3.5: Hybrid-Attention Diffusion Language Models. Code: https://github.com/AntonXue/dQwen Needs a CUDA GPU and transformers>=5.13 (tested with torch 2.7.1+cu128, flash-linear-attention 0.5.1). generate decodes the whole canvas at once, committing positions above a confidence threshold (tau=0.9); pass blocklength=32 for left-to-right block decoding, or tau=None, stepsperblock=k for a fixed budget. The 50B-token checkpoint from the paper is…

Model · Text generation

nexgen-flash-v1-merged-averaged

Sampson Kontomah

basemodel: - Qwen/Qwen3.5-9B pipelinetag: text-generation libraryname: transformers

Ask anything, get a how-to article. WikiQwen-9B is Qwen3.5-9B fine-tuned to answer every message the same way: as a tidy, step-by-step how-to article in markdown, with a picture caption above each step. Hand those captions to WikiQwen-Illustrator and they become illustrations. Send it a question ("how do I keep basil alive?"), a problem ("my bike chain keeps falling off") or just "hi", and it sends back a guide. Every reply has the same shape: - a # How to … title and a short intro - one or more ## Method N: or ## Part N: sections, or a single ## Steps - an [IMAGE: caption] line above each step, written for the Illustrator to draw - steps written as N. Bold summary. Details…, with bullets…

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Model · Text generation

JEV-9B-MLX-8bit

Nativ

MLX conversion of autotrust/JEV-9B for mlx-vlm, with the System 1 LoRA (including its lmhead update) merged and lmhead, the decision readout, kept unquantized. JEV is a decision model: it returns a probability for every option of a question in one forward pass and does not generate text. JEV support is not yet in an mlx-vlm release

Model · Text generation

JEV-9B-MLX-NVFP4

Nativ

MLX conversion of autotrust/JEV-9B for mlx-vlm, with the System 1 LoRA (including its lmhead update) merged and lmhead, the decision readout, kept unquantized. JEV is a decision model: it returns a probability for every option of a question in one forward pass and does not generate text. JEV support is not yet in an mlx-vlm release