Model · Text generation
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…
Open weights
apache-2.0
9B parameters
262,144 tokens
transformers
basemodel: - Qwen/Qwen3.5-9B pipelinetag: text-generation libraryname: transformers
Open weights
apache-2.0
9B parameters
262,144 tokens
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…
Open weights
cc-by-nc-sa-4.0
9B parameters
262,144 tokens
transformers
How do I pronounce the model's name? Watch a Youtube tutorial IDEFICS (Image-aware Decoder Enhanced à la Flamingo with Interleaved Cross-attentionS) is an open-access reproduction of Flamingo, a closed-source visual language model developed by Deepmind. Like GPT-4, the multimodal model accepts arbitrary sequences of image and text inputs and produces text outputs. IDEFICS is built solely on publicly available data and models. The model can answer questions about images, describe visual contents, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. IDEFICS is on par with the original closed-source model on various image-text benchmarks…
Open weights
other
8.9B parameters
2,048 tokens
transformers
Standard One scores a bounded set of answers for a supplied scenario and returns probabilities through POST /v1/systemone. It does not generate free-form response text. This repository contains the merged BF16 8B checkpoint and the server code. In the reported served evaluations, 8B scores higher than 3B on the public standard and hard tiers; 3B has a lower median latency on the measured short-request profile. See Benchmarks for the measurement conditions and limitations. The figure combines results from different measurement paths. See Benchmarks for served versus offline conditions; measured 24–26 September 2026. - Send a state and a bounded rubric to receive probabilities for the…
Open weights
apache-2.0
8.9B parameters
262,144 tokens
transformers
StandardOne-8B-FP8 is an FP8 (compressed-tensors, float8e4m3 weights, dynamic per-token activations) quantization of the released StandardOne-8B decision model. The language-model linear projections (q/k/v/o, gate/up/down) are quantized per-channel FP8 E4M3 with dynamic FP8 activations (llm-compressor's data-free FP8DYNAMIC recipe, no calibration data required); the vision tower, multi-modal projector, embeddings and lmhead are left unquantized in BF16. It was produced from source revision e88423700bb5ab9b2f50e176cf19825914345272 of StandardOne-8B on 2026-09-25 using llm-compressor 0.14.0 (torch 2.14.0, transformers 5.17.0, compressed-tensors 0.19.0); results below. Served through SGLang…
Open weights
apache-2.0
8.9B parameters
262,144 tokens
transformers
