bert-base-chinese
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bert-base-chinese-ner
ckiplabThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). - Mu Yang at CKIP (Author & Maintainer) For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
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View details ↗bert-base-chinese-pos
ckiplabThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).
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View details ↗bert-base-chinese-ws
ckiplabThis project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). - Mu Yang at CKIP (Author & Maintainer) For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.
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View details ↗bert-tiny-chinese
ckiplab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 ↗gpt2-base-chinese
ckiplab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 ↗gpt2-tiny-chinese
ckiplab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-4-26B-A4B-it-NVFP4
cklausNVFP4-quantized version of google/gemma-4-26B-A4B-it in **modelopt format**, optimized for vLLM on NVIDIA Blackwell (SM120) GPUs. Other available NVFP4 quantizations of this model quantize attention layers too, which causes: 1. Quality degradation from aggressive attention quantization 2. vLLM's QKV fusion scale bug (fused projections take `max()` of global scales, causing underflow) 1. Started from RedHatAI/gemma-4-26B-A4B-it-NVFP4 (compressed-tensors format) 2. Dequantized all 115 self-attention projections (q/k/v/o across 30 layers) back to BF16 using FP4-E2M1 lookup table 3. Converted from compressed-tensors to modelopt format: - `weight_packed` -> `weight` (same data) - `weight_global_scale` (divisor) -> `weight_scale_2` (1/divisor) - `input_global_scale` (divisor) -> `input_scale` (1/divisor) 4. Updated quantization config to match NVIDIA's modelopt pattern with proper `exclude_modules`
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View details ↗laya-browser
cklxx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 ↗anything-v3.0
ckpt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 ↗In-Context-LoRA
ckpt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 ↗ModelScope
ckpt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 ↗sd15
ckpt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 ↗ruri-base
cl-nagoya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 ↗ruri-base-v2
cl-nagoya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 ↗ruri-large
cl-nagoya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 ↗ruri-large-v2
cl-nagoya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 ↗ruri-reranker-base
cl-nagoya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 ↗ruri-reranker-large
cl-nagoya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 ↗ruri-reranker-small
cl-nagoya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 ↗ruri-small
cl-nagoya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 ↗ruri-v3-130m
cl-nagoya**Ruri v3** is a general-purpose Japanese text embedding model built on top of **ModernBERT-Ja**. Ruri v3 offers several key technical advantages: - **State-of-the-art performance** for Japanese text embedding tasks. - **Supports sequence lengths up to 8192 tokens** - Previous versions of Ruri (v1, v2) were limited to 512. - **Expanded vocabulary of 100K tokens**, compared to 32K in v1 and v2 - The larger vocabulary make input sequences shorter, improving efficiency. - **Integrated FlashAttention**, following ModernBERT's architecture - Enables faster inference and fine-tuning. - **Tokenizer based solely on SentencePiece** - Unlike previous versions, which relied on Japanese-specific BERT tokenizers and required pre-tokenized input, Ruri v3 performs tokenization with SentencePiece only—no external word segmentation tool is required. Additionally, if your GPUs support Flash Attention 2, we recommend using our models with Flash Attention 2. - **Model Type:** Sentence Transformer - **Base model:** cl-nagoya/ruri-v3-pt-130m - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 512 - **Similarity Function:** Cosine Similarity - **Language:** Japanese - **License:** Apache 2.0 - **Paper:** https://arxiv.org/abs/2409.07737
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View details ↗ruri-v3-30m
cl-nagoya**Ruri v3** is a general-purpose Japanese text embedding model built on top of **ModernBERT-Ja**. Ruri v3 offers several key technical advantages: - **State-of-the-art performance** for Japanese text embedding tasks. - **Supports sequence lengths up to 8192 tokens** - Previous versions of Ruri (v1, v2) were limited to 512. - **Expanded vocabulary of 100K tokens**, compared to 32K in v1 and v2 - The larger vocabulary make input sequences shorter, improving efficiency. - **Integrated FlashAttention**, following ModernBERT's architecture - Enables faster inference and fine-tuning. - **Tokenizer based solely on SentencePiece** - Unlike previous versions, which relied on Japanese-specific BERT tokenizers and required pre-tokenized input, Ruri v3 performs tokenization with SentencePiece only—no external word segmentation tool is required. Additionally, if your GPUs support Flash Attention 2, we recommend using our models with Flash Attention 2. - **Model Type:** Sentence Transformer - **Base model:** cl-nagoya/ruri-v3-pt-30m - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 256 - **Similarity Function:** Cosine Similarity - **Language:** Japanese - **License:** Apache 2.0 - **Paper:** https://arxiv.org/abs/2409.07737
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