ProSST-2048
AI4Protein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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cyner-xlm-roberta-base
AI4Sec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 ↗Cheers
ai9stars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 ↗G9v3-39A5B
AI9Stars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 ↗G9v3-3B
ai9stars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 ↗ComfyPod-Models
AiAngelGalleryUnchanged copies of the model files the free **AI Angel ComfyPod** RunPod template downloads, laid out like ComfyUI's `models/` folder (`text_encoders/`, `vae/`, `loras/`, …). They are re-hosted here so the template does not break when an upstream repository moves or deletes a file. **Nothing was modified**: every file is byte-identical to its source (same SHA-256, checked before the template switched to it). Template and guide: https://www.thepexcel.com/aiangel-comfypod/ · source: https://github.com/ThepExcel/AiAngelComfyPod All credit belongs to the original authors below. If you are an author and want a file taken down, open a discussion on this repository.
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View details ↗Huihui-Qwen3.8-27B-abliterated-GGUF
aiartoriousThis is an uncensored version of Qwen/Qwen3.8-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. The newly added **Huihui-Qwen3.8-27B-abliterated-Underdog-Saluki** series come from ConwayResearch/Underdog-Saluki-27B-1.0 and ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF. Only layers **22 to 52 (0-based indexing)** have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. Due to ConwayResearch/Underdog-Saluki-27B-1.0 not providing an imatrix file, we will attempt to perform ablation by mixing the GGUF weights of Swift-1.5 and ConwayResearch/Underdog-Saluki-27B-1.0.
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View details ↗TinyLlama-1.1B-Chat-v1.0-x2-MoE
AIChenKai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 ↗answerdotai-ModernBERT-base-ai-detector
AICodexLabThis model is a fine-tuned version of answerdotai/ModernBERT-base on the AI vs Human Text Classification dataset, DAIGT V2 Train Dataset. This model is based on **ModernBERT-base**, a lightweight and efficient BERT-based model. It has been fine-tuned for **AI-generated vs Human-written text classification**, allowing it to distinguish between texts written by **AI models (ChatGPT, DeepSeek, Claude, etc.)** and human authors.
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View details ↗**Uncensored Qwen3.5 9B (HauhauCS Aggressive) · NVFP4 quantized · MTP head grafted · vision-capable** A hand-built GGUF of HauhauCS's uncensored Qwen3.5-9B fine-tune, quantized with a custom NVFP4 recipe for Blackwell GPUs, with the Multi-Token-Prediction (MTP) head grafted in from the upstream base model, plus the original vision projector (mmproj) for image input. The quantization is a **mixed-precision recipe**, not a single uniform type. It was built with llama.cpp's `--tensor-type-file` (regex → type) plus an imatrix generated from ~144 KB of mixed calibration text (PPL ≈ 5.18):
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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 ↗qwen3-32b-dflash-en-zh
AICP-Labs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 ↗clm-200m
AICrossSim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 ↗This is a fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` from sentence-transformers model with Semantic Textual Similarity Benchmark extended to 15 languages: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering, semantic search and measuring the similarity between two sentences. This model is fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` for semantic textual similarity with multilingual data. The dataset used for this fine-tuning is STSb extended to 15 languages with Google Translator. For mantaining data quality the sentence pairs with a confidence value below 0.7 were dropped. The extended dataset is available at GitHub. The languages included in the extended version are: ar, cs, de, en, es, fr, hi, it, ja, nl, pl, pt, ru, tr, zh-CN, zh-TW. The pooling operation used to condense the word embeddings into a sentence embedding is mean pooling (more info below). Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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View details ↗star
AIDA-UPM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 ↗AnimeGen-I2V
aidealab**AnimeGen I2V** is a image-to-video generation model specialized for Japanese anime-style video generation. AnimeGen I2V was developed by **AIdeaLab, Inc.** as part of its effort to build high-quality generative AI models for animation and video production. The model was developed with support from **GENIAC — Generative AI Accelerator Challenge**, a Japanese government-backed project led by the Ministry of Economy, Trade and Industry of Japan (METI) and the New Energy and Industrial Technology Development Organization (NEDO), whose goal is to strengthen domestic capabilities in generative AI development. AnimeGen I2V is based on **Wan 2.2 I2V A14B** and further trained to improve anime-style motion, character rendering, color composition, and visual consistency for animation-oriented use cases.
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View details ↗AnimeGen-T2V
aidealab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 ↗RadJEPA
AIDElab-IITBombay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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