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plamo-2-translate-GGUF
mitmul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 ↗voxprint-gemma4-31b
Mitroshenkov87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 ↗voxprint-huihui-qwen36-27b
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View details ↗voxprint-mirror-mivolo-v2
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View details ↗voxprint-mirror-nsfw-image-detection
Mitroshenkov87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 ↗voxprint-mirror-siglip2-so400m-patch14-384
Mitroshenkov87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 ↗voxprint-t-lite-it-2.1
Mitroshenkov87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 ↗elan-mt-bt-en-ja
Mitsua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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Mitsua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 ↗mitsua-diffusion-one
Mitsua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 ↗mahjonglm-10m
mitsutani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 ↗bytellama-43m-cc
mittagessenByteLlama is a series of tiny byte-level language models built on the Llama architecture. Instead of a learned subword tokenizer, ByteLlama operates directly on raw UTF-8 bytes, using a simple octet tokenizer with a vocabulary size of 512. All models are trained on the **Historical and Cultural** subset of the Common Corpus, i.e. the open culture and pre-1900 filtered portion. Each model has been trained on 100 billion tokens (100,000 steps with an effective batch size of 512 and a sequence length of 2048). The models are trained with use in the party in mind. They expect a **language token** as the first input after the BOS token, indicating the language of the follow-up prompt. The tokenizer supports over 200 languages identified by ISO 639-3 codes. Given the distribution of languages in the training corpus it is unknown which languages the model has actually seen during training.
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View details ↗zenz-v2.5-medium
Miwa-Keita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 ↗zenz-v3.1-small
Miwa-Keita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 ↗zenz-v3.1-small-gguf
Miwa-Keita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 ↗zenz-v3.2-small-gguf
Miwa-Keita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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Miwa-Keita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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Miwa-Keita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 ↗deepset-mxbai-embed-de-large-v1
mixedbread-ai🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. Get in touch for access. This model is a powerful open-source German/English embedding model developed by Mixedbread in collaboration with deepset. It's built upon intfloat/multilingual-e5-large and trained using the AnglE loss. Read more details in our blog post. **In a bread loaf**: - State-of-the-art performance - Supports both binary quantization and Matryoshka Representation Learning (MRL). - Fine-tuned on 30+ million pairs of high-quality German data - Optimized for retrieval tasks - Supported Languages: German and English. - Requires a prompt: `query: {query}` for the query and `passage: {doc}` for the document
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View details ↗mxbai-colbert-large-v1
mixedbread-ai🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. Get in touch for access. This is our first English ColBERT model, which is built upon our sentence embedding model mixedbread-ai/mxbai-embed-large-v1. You can learn more about the models in our blog post. This checkpoint also loads with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`.
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View details ↗mxbai-edge-colbert-v0-17m
mixedbread-ai🍞 Looking for a simple end-to-end retrieval solution? Meet Mixedbread Search , our multi-modal and multi-lingual search solution. This model is a lightweight, 17 million parameter ColBERT with a projection dimension of 48. It is built on top of Ettin-17M, meaning it benefits from all of ModernBERT's architectural efficiencies. Despite this extreme efficiency, it is the best-performer "edge-sized" retriever, outperforming ColBERTv2 and many models with over 10 times more parameters. It can create multi-vector representations for documents of up to 32,000 tokens and is fully compatible with the PyLate library. This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:
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View details ↗mxbai-edge-colbert-v0-32m
mixedbread-ai🍞 Looking for a simple end-to-end retrieval solution? Meet Mixedbread Search , our multi-modal and multi-lingual search solution. This model is a lightweight, 32 million parameter ColBERT with a projection dimension of 64. It is built on top of Ettin-32M, meaning it benefits from all of ModernBERT's architectural efficiencies. Despite this extreme efficiency, it is the best-performer "edge-sized" retriever, outperforming ColBERTv2 and many models with over 10 times more parameters. It can create multi-vector representations for documents of up to 32,000 tokens and is fully compatible with the PyLate library. This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:
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