d1-3B-LiteRT
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GLiFormer-Large-NER-LiteRT
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View details ↗GLiNER2.5-Decide-LiteRT
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View details ↗GLiNER2.5-Small-LiteRT
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View details ↗Kev-0.8B-LiteRT
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View details ↗Kev-4B-LiteRT
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View details ↗Laya-English-LiteRT
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View details ↗laya-LiteRT
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View details ↗Laya-Multilingual-LiteRT
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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 ↗AlignScore-large-hf
liuyanyi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 ↗clef-flash-GGUF
Livesport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 ↗Decision-2.0-Lux-9B-GGUF
Livesport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 ↗rune-26b-a4b-v3-GGUF
Livesportllama.cpp builds of **Rune 26B-A4B v3**, the decision model by Invergent. Each file is measured against the original bf16 weights. Livesport serves **IQ3_S-dyn** in production on **2× Quadro RTX 4000 8 GB** behind a Jev/SystemOne-compatible `/v1/systemone` API. Rune does not generate text. Given a *state* and a question with a set of options, it returns a probability for every option, read from the logits of the option letters at the first generated position. The upstream card describes the model, its training protocol and its Decision Index results. This card covers only what is specific to these quantizations. **Text only:** the files contain the language model, without Gemma 4's vision tower, so image states are not supported. The first version of this repository (one IQ3_M in three shards, KL 0.0450) is at revision `4990396`. IQ3_S-dyn replaces it: 1.2 GB larger, with half the KL against bf16.
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View details ↗tl_calamancy_md
ljvmiranda921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 ↗bert-base-japanese-v3-jnli
llm-book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 ↗bert-base-japanese-v3-jsts
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View details ↗bert-base-japanese-v3-marc_ja
llm-book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 ↗「大規模言語モデル入門」の第6章で紹介している固有表現認識のモデルです。 cl-tohoku/bert-base-japanese-v3をllm-book/ner-wikipedia-datasetでファインチューニングして構築されています。
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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 ↗privacy-filter-GGUF
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View details ↗privacy-filter-multilingual-GGUF
LocalAI-ioGGUF conversion of `OpenMed/privacy-filter-multilingual`, a multilingual PII **token-classification** model (a fine-tune of `openai/privacy-filter`). It labels every token with a BIOES tag over **54 PII categories (217 classes)** across **16 languages**, so it can be served locally with **no Python** as the encoder/NER tier of a PII redactor. For the full model description, label space, evaluation, limitations, and citations, see the **source model card** — this card only covers the GGUF packaging and how to run it. This GGUF uses a **custom architecture, `openai-privacy-filter`**, that is not (yet) part of upstream llama.cpp. It runs on:
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View details ↗privacy-filter-nemotron-GGUF
LocalAI-ioGGUF conversion of `OpenMed/privacy-filter-nemotron`, a **fine-grained** PII **token-classification** model — a fine-tune of `openai/privacy-filter` on the `nvidia/Nemotron-PII` dataset. It labels every token with a BIOES tag over **55 PII categories (221 classes)** in a single forward pass, then decodes coherent spans with a constrained Viterbi procedure — so it can be served locally with **no Python** as the encoder/NER tier of a PII redactor. Where the base `openai/privacy-filter` covers 8 coarse categories, this fine-tune trades multilingual breadth for **category depth**: 55 fine-grained English categories (first/last name, government IDs, financial, healthcare, vehicle, digital, …). For the full model description, label space, evaluation, limitations, and citations, see the **source model card** — this card only covers the GGUF packaging and how to run it.
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