UltraBERT-Embed-150M
dmis-lab基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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Decensored (abliterated) **Qwen3.6-35B-A3B**, the **balanced** operating point: refusals removed at the lowest damage to the model. Built with a fork of Heretic that searches against a **perplexity objective** instead of first-token KL — that is what makes this build cheaper than the previous release at the same or fewer refusals. Sibling: `-max-v2` is the more aggressive point (fewer refusals, more damage); it also loses more facts, so **balanced is the recommended build**. Method and figures: the study repo. The refusal direction is removed from every projection that writes to the residual stream — full and linear attention outputs, the 256 routed experts (fused), and the always-on shared expert. The multi-token-prediction layer is restored verbatim from the base model, so that branch is unmodified. All builds carry the full 1045 tensors.
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View details ↗Qwen3.6-35B-A3B-Heretic-MOE-v3
DmitryDB基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗flan-t5-large-onnx-js-quantized
dmmagdal基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗comfyui-nsfw-model-pack
dmtryyy基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗gene_eng_gpt2_v0
dnagpt基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DNA-VL-STEER-2B
dnotitia基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3-0.6B
dnotitia基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3-1.7B
dnotitia基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗brad
doberbober基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗msmarco-t5-base-v1
doc2query基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗msmarco-t5-small-v1
doc2query基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗CodeFormula
docling-project基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗CodeFormulaV2
docling-projectThe **Code Formula Model** processes an image of a code snippet or formula at 120 DPI and outputs its content. - **Code Snippets**: The model identifies the programming language and outputs the code repsecting the indendation shown in the given image. The output format will be: " \_> \ " Example: " System.out.println("Hello World.");" This model was trained using the following two datasets: 1. https://huggingface.co/datasets/ds4sd/SynthFormulaNet 2. https://huggingface.co/datasets/ds4sd/SynthCodeNet
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View details ↗DeskForge-Gemma4-E4B
docling-project**DeskForge-Gemma4-E4B** is Gemma 4 E4B (instruction-tuned) fine-tuned on 200K grounding examples from DeskForge-1M. Given a desktop screenshot and a natural-language instruction, it answers with the next action as a `pyautogui` call whose coordinates are fractions of the screen. **Live demo** · **Project page** · **Dataset** · **Code** · **Paper** · Also available: DeskForge-Qwen3.5-4B **Grounding on DeskForge-1M held-out conditions** (accuracy, %). New Scenes are unseen scenes with seen attributes; Theme, App and Resolution hold an appearance preset, three applications and a display resolution out of training entirely.
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View details ↗DeskForge-Qwen3.5-4B
docling-project**News — v1.1:** Updated fine-tuning with additional GUI workflows improves performance on all five external grounding benchmarks. The original paper checkpoint remains available at revision `v1.0`. **DeskForge-Qwen3.5-4B** is Qwen3.5-4B fine-tuned for desktop GUI grounding and next-action prediction using grounding and GUI-workflow supervision. Given a desktop screenshot and a natural-language instruction, it answers with the next action as a `pyautogui` call whose coordinates are fractions of the screen. **Live demo** · **Project page** · **Dataset** · **Code** · **Paper** · Also available: DeskForge-Gemma4-E4B
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View details ↗docling-layout-egret-large
docling-project📄 For an in-depth description of the model architecture, training datasets, and evaluation methodology, please refer to our technical report: **"Advanced Layout Analysis Models for Docling"**, Nikolaos Livathinos *et al.*, 🔗 https://arxiv.org/abs/2509.11720
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View details ↗docling-layout-egret-medium
docling-project基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗docling-layout-egret-xlarge
docling-project📄 For an in-depth description of the model architecture, training datasets, and evaluation methodology, please refer to our technical report: **"Advanced Layout Analysis Models for Docling"**, Nikolaos Livathinos *et al.*, 🔗 https://arxiv.org/abs/2509.11720
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View details ↗docling-layout-heron
docling-project🚀 **`heron`** is the **default layout analysis model** of the Docling project, designed for robust and high-quality document layout understanding. 📄 For an in-depth description of the model architecture, training datasets, and evaluation methodology, please refer to our technical report: **"Advanced Layout Analysis Models for Docling"**, Nikolaos Livathinos *et al.*, 🔗 https://arxiv.org/abs/2509.11720
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View details ↗docling-layout-heron-101
docling-project🚀 **`heron-101`** is a Document Layout Analysis Model used in the Docling project. 📄 For an in-depth description of the model architecture, training datasets, and evaluation methodology, please refer to our technical report: **"Advanced Layout Analysis Models for Docling"**, Nikolaos Livathinos *et al.*, 🔗 https://arxiv.org/abs/2509.11720
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