breastcancer-ultrasound-ViT
ITSheep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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Auro-110M
ItsnotAilabs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 ↗GHOST-2.7B
ItsnotAilabs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 ↗HIM-3B
ItsnotAilabs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 ↗Spark-X2.5-4B-Heretic-GGUF
ItsOkayNow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 ↗sawyer-0.5b
itsrishub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 ↗Scar-8B-GGUF
ItzRustam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 ↗mms-tts-tachebdant-504
iukocha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 ↗DeepSeek-V4.1-Flash-DS4-IQ2
ivanfioravanti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-Flash-Next-DS4-IQ2
ivanfioravanti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-Flash-Next-DS4-Q4
ivanfioravantiDS4 quantization of Qwen/Qwen3.8-Flash-Next, built from the official BF16 checkpoint at revision `de4b8e4d43b917e7706784d8bb445c9af86a3540` with an importance-matrix calibrated Q4_K routed-expert recipe. The current release is the **qwen4exp-schema** structure: standard llama.cpp GGUF tensor naming that the DS4 engine (Metal graph) loads directly, with the 51B-value PLE n-gram table kept in the external sidecar so it never has to be memory-resident. The main GGUF includes the 48 trunk layers and one **embedded MTP block**. This is the only model version distributed here: use the same file for ordinary decoding or enable speculative decoding with `--mtp`. The external PLE table is required in both cases.
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View details ↗Qwen3.8-27B-AWQ-INT4-Q4_0-GGUF
IvanKrastevAdventics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 ↗llm-course-hw1
ivanz24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 ↗genderage2
ivensamdh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 ↗Ivme-Conversate-v3-Base
IvmeLabs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 ↗bioBIT
IVN-RIN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 ↗pyannote-segmentation-3.0
ivrit-ai--- tags: - pyannote - pyannote-audio - pyannote-audio-model - audio - voice - speech - speaker - speaker-diarization - speaker-change-detection - speaker-segmentation - voice-activity-detection - overlapped-speech-detection - resegmentation license: mit inference: false
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View details ↗pyannote-speaker-diarization-3.1
ivrit-aiThis model is a fork of pyannote/speaker-diarization-3.1. Read the original model page for all details, including citation format.
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View details ↗whisper-large-v3
ivrit-aiLanguage detection capability of this model has been degraded during training - it is intended for mostly-hebrew audio transcription. Language token should be explicitly set to Hebrew. Additionally, the tanslation task was not trained and also degraded. This model would not be able to translate in any reasonable capacity. Please follow the original model card for usage details - replacing with this model name. You can also fine other weight formats ad quantizations on the ivrit ai HF page.
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View details ↗whisper-large-v3-ct2
ivrit-aiThis is a Ctranslate2 (Faster Whisper) weight format of the ivrit-ai/whisper-large-v3 Please follow the Faster Whisper usage details here and use this model name.
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View details ↗whisper-large-v3-turbo
ivrit-aiThis model is a Hebrew finetune (continued training) of the OpenAI Whisper Large v3 Turbo model. - **Developed by:** ivrit-ai - **Language(s) (NLP):** Hebrew - **License:** Apache-2.0 - **Finetuned from model** openai/whisper-large-v3-turbo - **Training Date** Apr 2025 Language detection capability of this model has been degraded during training - it is intended for mostly-hebrew audio transcription. Language token should be explicitly set to Hebrew.
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View details ↗whisper-large-v3-turbo-ct2
ivrit-aiThis is ivrit.ai's faster-whisper model, based on the ivrit-ai/whisper-large-v3-turbo Whisper model. Training data includes 295 hours of volunteer-transcribed speech from the ivrit-ai/crowd-transcribe-v5 dataset, as well as 93 hours of professional transcribed speech from other sources.
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View details ↗whisper-large-v3-turbo-onnx
ivrit-ai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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