yayoi_mix
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 85001
yomitoku-text-recognizer-parseq-open-beta
KotaroKinoshitaThis model has been pushed to the Hub using the PytorchModelHubMixin integration: - Library: [More Information Needed] - Docs: [More Information Needed]
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查看详情 ↗infinite_compute_distillation
kothasuhas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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查看详情 ↗uzbek_stt_v1
Kotib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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查看详情 ↗kotoba-whisper-bilingual-v1.0
kotoba-tech_Kotoba-Whisper-Bilingual_ is a collection of distilled Whisper models trained for - **Japanese ASR** - **English ASR** - **Speech-to-text translation (Japanese -> English)** - **Speech-to-text translation (English -> Japanese)** developed through the collaboration bewteen Asahi Ushio and Kotoba Technologies. Following the original work of distil-whisper (Robust Knowledge Distillation via Large-Scale Pseudo Labelling), we employ OpenAI's Whisper large-v3 as the teacher model for Japanese and English ASR, while we translate the transcription into English and Japanese by external LLM to obtain training dataset for speech-to-text translation. We employ ReazonSpeech for Japanese ASR and Japanese speech to English text translation, and Multilingual LibriSpeech for English ASR and English speech to Japanese text translation. Kotoba-whisper-bilingual's loss objective consists of cross-entropy on both of ASR and translation tasks, while KL divergence loss only for ASR task. The student model consists the full encoder of the teacher large-v3 model and the decoder with two layers initialized from the first and last layer of the large-v3 model. As kotoba-whisper uses the same architecture as distil-whisper/distil-large-v3, it inherits the benefit of the improved latency compared to openai/whisper-large-v3 (**6.3x faster than large-v3**, see the table below taken from distil-whisper/distil-large-v3).
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查看详情 ↗kotoba-whisper-v1.1
kotoba-tech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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查看详情 ↗kotoba-whisper-v2.0
kotoba-tech_Kotoba-Whisper_ is a collection of distilled Whisper models for Japanese ASR, developed through the collaboration bewteen Asahi Ushio and Kotoba Technologies. Following the original work of distil-whisper (Robust Knowledge Distillation via Large-Scale Pseudo Labelling), we employ OpenAI's Whisper large-v3 as the teacher model, and the student model consists the full encoder of the teacher large-v3 model and the decoder with two layers initialized from the first and last layer of the large-v3 model. Kotoba-Whisper is **6.3x faster than large-v3**, while retaining as low error rate as the large-v3. As successor of our first model, kotoba-whisper-v1.0, we release ***kotoba-whisper-v2.0*** trained on the `all` subset of ReazonSpeech (the largest speech-transcription paired dataset in Japanese extracted from Japanese TV audio recordings), which amounts 7,203,957 audio clips (5 sec audio with 18 text tokens in average) after those transcriptions more than 10 WER are removed (see WER Filter for detail). The model was trained for 8 epochs with batch size 256 with sampling rate of 16kHz, and the training and evaluation code to reproduce kotoba-whisper is available at https://github.com/kotoba-tech/kotoba-whisper. Kotoba-whisper-v2.0 achieves better CER and WER than the openai/whisper-large-v3 in the in-domain held-out test set from ReazonSpeech, and achieves competitive CER and WER on the out-of-domain test sets including JSUT basic 5000 and the Japanese subset from CommonVoice 8.0 (see Evaluation for detail).
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查看详情 ↗kotoba-whisper-v2.0-faster
kotoba-techThis repository contains the conversion of kotoba-tech/kotoba-whisper-v2.0 to the CTranslate2 model format. We measure the inference speed of different kotoba-whisper-v2.0 implementations with four different Japanese speech audio on MacBook Pro with the following spec: - Apple M2 Pro - 32GB - 14-inch, 2023 - OS Sonoma Version 14.4.1 (23E224) Also, currently whisper.cpp and faster-whisper support the sequential long-form decoding, and only Huggingface pipeline supports the chunked long-form decoding, which we empirically found better than the sequnential long-form decoding.
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查看详情 ↗kotoba-whisper-v2.1
kotoba-tech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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查看详情 ↗kotoba-whisper-v2.2
kotoba-tech_Kotoba-Whisper-v2.2_ is a Japanese ASR model based on kotoba-tech/kotoba-whisper-v2.0, with additional postprocessing stacks integrated as `pipeline`. The new features includes (i) speaker diarization with diarizers and (ii) adding punctuation with punctuators. The pipeline has been developed through the collaboration between Asahi Ushio and Kotoba Technologies Kotoba-Whisper-v2.2 is supported in the Hugging Face 🤗 Transformers library from version 4.39 onwards. To run the model, first install the latest version of Transformers. To load pre-trained diarization models from the Hub, you'll first need to accept the terms-of-use for the following two models: 1. pyannote/segmentation-3.0 2. pyannote/speaker-diarization-3.1
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查看详情 ↗DeepSeek-V4-Pro-Qwen3.5-9B-MTP-GGUF
Kottk14-02-09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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查看详情 ↗gutsy
kouhxp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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查看详情 ↗This repository contains a YOLOv8 object detection model trained to detect **license plates** in real-world images. The model was trained using the Ultralytics YOLOv8 framework and can be deployed for real-time applications such as surveillance, traffic monitoring, and vehicle identification. - **Architecture**: YOLOv8n (Nano variant) - **Framework**: Ultralytics YOLOv8 - **Task**: Object Detection - **Classes**: 1 (`license_plate`) - **Input resolution**: 640×640 - **File**: `best.pt`
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查看详情 ↗kova-tts-1
kova-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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Koxae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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查看详情 ↗FLUX.2-klein-9B
kp-forksThe FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, **delivering state-of-the-art quality with end-to-end inference in as low as under a second**. Built for applications that require real-time image generation without sacrificing quality. FLUX.2 [klein] 9B is a 9 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities. Our flagship small model. Defines the Pareto frontier for quality vs. latency across text-to-image, single-reference editing, and multi-reference generation. Matches or exceeds models 5x its size—in under half a second. Built on a 9B flow model with 8B Qwen3 text embedder, step-distilled to 4 inference steps.
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查看详情 ↗Flux-uncensored
kp-forks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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查看详情 ↗nunchaku-qwen-image-edit
kp-forks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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查看详情 ↗PaperPilot-Qwen-0.5B-Instruct
kpakkiragari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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查看详情 ↗KPF-bert-ner
KPF- 빅카인즈랩 인사이드 메뉴의 개체명 분석에서 사용된 개체명 인식 모델이다. - 사용 방법에 대한 안내 및 코드는 KPF-bigkinds github에서 확인할 수 있습니다. 한국언론진흥재단이 개발한 kpf-BERT 모델을 기반으로 NER(Named Entity Recognition) task를 수행할 수 있는 kpf-BERT-ner 모델을 설계 및 개발한다. NER은 이름을 가진 객체를 인식하는 것을 의미한다. 한국정보통신기술협회가 제공하는 정보통신용어사전에 따르면 NER은 다음과 같다. “NER은 미리 정의해둔 사람, 회사, 장소, 시간, 단위 등에 해당하는 단어(개체명)를 문서에서 인식하여 추출 분류하는 기법. 추출된 개체명은 인명(person), 지명(location), 기관명(organization), 시간(time) 등으로 분류된다. 개체명 인식은 정보 추출을 목적으로 시작되어 자연어 처리, 정보 검색 등에 사용된다.” 실무적으로 표현하면 ‘문자열을 입력으로 받아 단어별로 해당하는 태그를 출력하게 하는 multi-class 분류 작업’이다. 본 과제에서는 kpf-BERT-ner 모델을 설계 및 개발하고 언론 기사를 학습하여 150개 클래스를 분류한다. - 한국어 데이터 셋은 모두의 말뭉치에서 제공되는 국립국어원 신문 말뭉치 추출 를 사용하였다.
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查看详情 ↗kas-4b
kpiya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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查看详情 ↗FHDR_Uncensored
kpsss34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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查看详情 ↗Buttercup-4x7B-GGUF
Kquant03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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查看详情 ↗CognitiveFusion2-4x7B-BF16
Kquant03This is an update to the original Cognitive Fusion. We intend to perform a fine-tune on it in order to increase its performance. The scale of a model is one of the most important axes for better model quality. Given a fixed computing budget, training a larger model for fewer steps is better than training a smaller model for more steps. Mixture of Experts enable models to be pretrained with far less compute, which means you can dramatically scale up the model or dataset size with the same compute budget as a dense model. In particular, a MoE model should achieve the same quality as its dense counterpart much faster during pretraining.
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。