mmBERT-base-norwegian
Fremtind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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BioLORD-2023
FremyCompanyThis model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD-2023 establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (EHR-Rel-B).
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View details ↗BioLORD-2023-C
FremyCompanyThis model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD-2023 establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (EHR-Rel-B).
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View details ↗BioLORD-2023-M
FremyCompanyThis model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD-2023 establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (EHR-Rel-B).
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View details ↗BioLORD-2023-M-Dutch-InContext-v1
FremyCompanyThis model supports embedding biomedical entities in both English and Dutch, but allows the in-context embedding of concepts, using the following template: It also supports embedding mentions without context, particularly in English. **NOTE:** Unlike other models in the series, this model uses the [CLS] token to embed the mention. Journal of the American Medical Informatics Association, 2024 François Remy, Kris Demuynck, Thomas Demeester view online
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View details ↗Nanbeige4.3-33B-cybersec-abliterated-GGUF
FRENCHFAKER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 ↗Reaper-Base
FrenzyMath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 ↗F5-Myanmar-TTS
freococo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 ↗MyanmarTTS
freococo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-Avalon-GGUF
freriadoc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 ↗WizardLM-7B-uncensored-GGUF
Freshboy242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 ↗Freya-TTS
freyavoiceFreyaTTS-small is a 183M-parameter Turkish text-to-speech model, and the open-source member of the FreyaTTS family. It is tokenizer-free at the character level (92 symbols, no phonemizer or G2P) and generates speech with a non-autoregressive conditional flow-matching DiT in the frozen AudioVAE2 latent space (25 Hz, 64-dim latents, 16 kHz encode / 48 kHz decode). Output is 48 kHz mono. **FreyaTTS-small** (this model) is our compact, Apache-2.0, self-hostable model, released in full: weights, inference code, and training pipeline. **FreyaTTS-large** is our production model, serving Turkish voice agents at Freya (YC S25) with higher naturalness and expressivity. It is available commercially rather than as open weights. For access, contact us at **dev@freyavoice.ai**.
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View details ↗repvgg_a0
frgfm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 ↗gemma-4-E2B-it-GGUF
fridaycandour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 ↗Meta-Llama-3.1-8B-Instruct-fp8
FriendliAI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 ↗Meta-Llama-3.1-8B-Instruct-int8
FriendliAIThis repo contains the Llama 3 8B Instruct model quantized to INT8 by FriendliAI, significantly enhancing its inference efficiency while maintaining high accuracy. Check out FriendliAI documentation for more details. 1. Sign in Friendli Suite. 2. Go to **User Settings > Tokens** and click **'Create new token'**. 3. Save your created token value. Container secret is a credential to launch our Friendli Container images. You should pass the container secret as an environment variable to run the container image.
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View details ↗Molmo-7B-O-0924
FriendliAI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 ↗Llama-3.2-1B-Instruct-pruned-h0.5-i0.5-a0.0-d0.0
friendshipkim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 ↗GigaChat3-10B-A1.8B-base-GGUF
FriskyFennec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 ↗Ling-3.0-tiny-base-30T-GGUF
FriskyFennec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 ↗HiTOP-QWEN4B-mlx-Q4
FritzStack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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