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AI Models 85003

• LazyMix-v4.0-inpainting↗

  TheImposterImposters

• fastvit_sa24.apple_in1k↗

  timm

• PE-Core-bigG-14-448↗

  timm

  Perception Encoder (PE) is a state-of-the-art encoder for image and video understanding trained via simple vision-language learning. It was introduced in "Perception Encoder: The best visual embeddings are not at the output of the network". **Model Overview**: Perception Encoder (PE) is a family of large-scale vision encoder models with state-of-the-art performance on a large variety of vision tasks. By using a robust contrastive pretraining recipe and finetuning on synthetically aligned videos, PE not only outperforms all existing models on classification and retrieval, but it also internally produces strong, general features that scale for downstream tasks. PE unlocks the ability for large-scale contrastive pretraining to transfer to downstream tasks with alignment tuning to capitalize on those general features. PE core is our base model trained with our robust image pretraining schedule and finetuned on the data generated by our synthetic video data engine.

• Qwen3.5-397B-A17B-MTP-GGUF↗

  unsloth

• Qwen3.8-27B-OptiMix-GGUF↗

  Vadim1700

• TinyBERT_SST2↗

  Vishnou

• Qwen3.8-27B-Palimpsest-GGUF↗

  xero0000

• sage-fredt5-distilled-95m↗

  ai-forever

  The model corrects spelling and punctuation errors and typos by bringing all the words in the text to the norm of the Russian language. Corrector is a distilled version of the original model that had been trained based on the FRED-T5-1.7B architecture. An extensive dataset with “artificial” errors was taken as a training corpus: the corpus was assembled on the basis of the Russian-language Wikipedia and transcripts of Russian-language videos, then typos and spelling errors were automatically introduced into it using the library SAGE. Below are automatic metrics for determining the correctness of the spell checkers. We compare our solution with both open automatic spell checkers and the ChatGPT family of models on all four available datasets: - **RUSpellRU**: texts collected from (LiveJournal), with manually corrected typos and errors; - **MultidomainGold**: examples from 7 text sources, including the open web, news, social media, reviews, subtitles, policy documents and literary works; - **MedSpellChecker**: texts with errors from medical anamnesis; - **GitHubTypoCorpusRu**: spelling errors and typos in commits from GitHub; **RUSpellRU** | Model | Pr. (spell) | Rec. (spell) | F1 (spell) | Pr. (punc) | Rec. (punc) | F1 (punc) | Pr. (case) | Rec. (case) | F1 (case) | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | sage-fredt5-distilled-95m | 83.5 | 74.8 | 78.9 | 86.8 | 80.6 | 83.6 | 94.4 | 92.5 | 93.5 | | sage-ai-service | 90.3 | 86.3 | 88.2 | 90.3 | 86.6 | 88.4 | 95.2 | 95.9 | 95.6 | | gpt-3.5-turbo | 33.6 | 58.5 | 42.7 | 85.9 | 64.6 | 73.7 | 84.9 | 73.9 | 79.0 | | gpt-4 | 54.9 | 76.7 | 64.0 | 84.0 | 82.3 | 83.2 | 91.5 | 90.2 | 90.9 |

• Qwen3-ASR-CoreML↗

  aufklarer

• Burnt-Toast_olmo-marvin-full-merged-GGUF↗

  bartowski