REDCELL-26B-A4B-OSINT-Cyber
0xSojalSec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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Uncensored-Cyber-Ornith-1.5-9B
0xSojalSec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 ↗UNSEEN_Gemma_4_26B_NSFW-GGUF
0xSojalSec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 ↗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 ↗Qwen3.8-27B-Kearuga
0xWhiteMage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 ↗Qwen3.8-27B-Kearuga-DFlash2
0xWhiteMage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 ↗ThinkingCap-Qwen3.8-27B-Kearuga-EXL3-4.00bpw
0xWhiteMage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 ↗LFM2.5-24B-A2B-EXL3-4bpw
0xzknw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 ↗Ornith-1.5-9B-MTPLX-4bit
0xzknw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 ↗punct_cap_seg_47_language
1-800-BAD-CODEThis model accepts as input lower-cased, unpunctuated, unsegmented text in 47 languages and performs punctuation restoration, true-casing (capitalization), and sentence boundary detection (segmentation). All languages are processed with the same algorithm with no need for language tags or language-specific branches in the graph. This includes continuous-script and non-continuous script languages, predicting language-specific punctuation, etc. This model is fun to play with, but the results could be better. I would recommend these newer, better models: * Better English model * Better Romance languages model * Better 47-language
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View details ↗punctuation_fullstop_truecase_english
1-800-BAD-CODEThis model accepts as input lower-cased, unpunctuated English text and performs in one pass punctuation restoration, true-casing (capitalization), and sentence boundary detection (segmentation). In contast to many similar models, this model can predict punctuated acronyms (e.g., "U.S.") via a special "acronym" class, as well as arbitarily-capitalized words (NATO, McDonald's, etc.) via multi-label true-casing predictions. **Widget note**: The text generation widget doesn't seem to respect line breaks. Instead, the pipeline inserts a new line token `\n` in the text where the model has predicted sentence boundaries (line breaks).
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View details ↗xlm-roberta_punctuation_fullstop_truecase
1-800-BAD-CODEThis is an `xlm-roberta` fine-tuned to restore punctuation, true-case (capitalize), and detect sentence boundaries (full stops) in 47 languages. If you want to just play with the model, the widget on this page will suffice. To use the model offline, the following snippets show how to use the model both with a wrapper (that I wrote, available from `PyPI`) and manual usuage (using the ONNX and SentencePiece models in this repo). The easiest way to use this model is to install punctuators:
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View details ↗LTX-2.3-Audio-Reactive-LORA
100percentrobot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 ↗extBanglaT5
101rakibulhasan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 ↗Phi-3.5-MoE-4bit-fp4
1024m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 ↗BiRefNet
1038lab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 ↗llama-joycaption-alpha-two
1038lab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 ↗llama-joycaption-beta-one
1038lab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 ↗Qwen-Image-Edit-2511-FP8
1038labThis repository contains the **FP8 quantized version** of the Qwen-Image-Edit-2511 model. It is designed for efficient inference while maintaining high-quality image editing capabilities. **Qwen-Image-Edit-2511 Enhances Character Consistency** In Qwen-Image-Edit-2511, character consistency has been significantly improved. The model can perform imaginative edits based on an input portrait while preserving the identity and visual characteristics of the subject. **Improved Multi-Person Consistency** While Qwen-Image-Edit-2509 already improved consistency for single-subject editing, Qwen-Image-Edit-2511 further enhances consistency in multi-person group photos—enabling high-fidelity fusion of two separate person images into a coherent group shot:
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View details ↗RMBG-2.0
1038lab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 ↗SDMatte
1038lab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 ↗segformer_clothes
1038lab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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