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Explore AI models 84842
Warlock-GLM-5.3-GGUF
ahnafnafee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 ↗urdu-sentiment-model
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View details ↗task-17-google-gemma-2b
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View details ↗Gemma-2-Ataraxy-Gemmasutra-9B-slerp-GGUF
AI-Art-Collab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 ↗giga-embeddings-0826-3b-gguf
ai-babai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 ↗giga-embeddings-0826-480m-gguf
ai-babai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 fork, not our model.** All credit for the abliterated build goes > to **huihui-ai**, > and for the base model to **Qwen (Qwen/Qwen3.8-27B)**. > This fork exists so the fleet's copy cannot be altered or pulled upstream, > and to carry the fit notes below. License: Apache-2.0 (per the source repo). The build was made by **abliteration** (refusal-direction removal, using Sumandora's remove-refusals-with-transformers) — the method and tool are documented on the source card. Note from the same card: several sub-series (Swift / Saluki / Ternary variants) ablate only layers 22–52; the headline UD-DW and plain series are the main line. **Recommended file for a 20 GB card:** `Huihui-Qwen3.8-27B-abliterated-UD-DW-Q4_K_M.gguf` (15.41 GiB).
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View details ↗Meta-Llama-3.1-8B-Instruct-GGUF
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View details ↗Meta-Llama-3-8B-Instruct-GGUF
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View details ↗FRED-T5-1.7B
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View details ↗FRED-T5-large
ai-forever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 ↗FRIDA
ai-foreverFRIDA is a full-scale finetuned general text embedding model inspired by denoising architecture based on T5. The model is based on the encoder part of FRED-T5 model and continues research of text embedding models (ruMTEB, ru-en-RoSBERTa). It has been pre-trained on a Russian-English dataset and fine-tuned for improved performance on the target task. For more model details please refer to our article (RU). The model's results are presented on the MTEB and rusBEIR leaderboards. The model can be used as is with prefixes. It is recommended to use CLS pooling. The choice of prefix and pooling depends on the task.
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View details ↗FRIDA-Decisions
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View details ↗kandinsky-videomae-large-camera-motion
ai-foreverVideoMAE model(`large`) variant that has been finetuned for multi-label video classification (a video can belong to multiple classes simultaneously) for camera motion classification on internal dataset. The model predicts `18` different camera motion `'arc_left', 'arc_right', 'dolly_in', 'dolly_out', 'pan_left', 'pan_right', 'pedestal_down', 'pedestal_up', 'roll_left', 'roll_right', 'static', 'tilt_down', 'tilt_up', 'truck_left', 'truck_right', 'undefined', 'zoom_in', 'zoom_out'` and and `3` shot type classes: `'pov', 'shake', 'track' ` . Model was trained to associate **entire video** with camera labels, not frame-level motions(!): [input video] -> label/labels (because multilabel) for all video. So, if this camera motion exists during all video frames model should predict this motion, otherwise it should predict `undefined`.
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View details ↗mGPT
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View details ↗mGPT-13B
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View details ↗Pollux-4B-Judge
ai-forever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 ↗ru-en-RoSBERTa
ai-foreverThe ru-en-RoSBERTa is a general text embedding model for Russian. The model is based on ruRoBERTa and fine-tuned with ~4M pairs of supervised, synthetic and unsupervised data in Russian and English. Tokenizer supports some English tokens from RoBERTa tokenizer. For more model details please refer to our article. The model can be used as is with prefixes. It is recommended to use CLS pooling. The choice of prefix and pooling depends on the task.
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View details ↗ruBert-base
ai-foreverThe model architecture design, pretraining, and evaluation are documented in our preprint: **A Family of Pretrained Transformer Language Models for Russian**. The model is pretrained by the SberDevices team. * Task: `mask filling` * Type: `encoder` * Tokenizer: `BPE` * Dict size: `120 138` * Num Parameters: `178 M` * Training Data Volume `30 GB`
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