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View details ↗DISCOVER & COMPARE
Explore AI models 2043
tiny-random-mt5
lewtun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 ↗MyAwesomeModel-TestRepo
lfeng03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-VL-Embedding-2B-AWQ-4bit
LifetimeMistake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-VL-Embedding-2B-NVFP4
LifetimeMistake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 ↗Agent-ModernColBERT
lightonai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 ↗answerai-colbert-small-v1
lightonaiThis is a answerdotai/answerai-colbert-small-v1 model converted to PyLate format. It maps sentences & paragraphs to sequences of 96-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval. First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
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View details ↗ColBERT-Zero
lightonai> 🎯 **TL;DR**: First large-scale fully pre-trained ColBERT model using only public data. Achieves **55.43 nDCG@10** on BEIR benchmark, outperforming GTE-ModernColBERT and GTE-ModernBERT trained on closed and stronger data. **New SOTA on BEIR for models Nomic's base models are pre-trained with asymmetric prompts (`search_query:` and `search_document:`). While ColBERT has its own asymmetric mechanism via `[Q]` and `[D]` markers, we found: - **Stripping pre-training prompts during fine-tuning** causes significant performance degradation. - **Adding prompts to a model not pre-trained with them** also hurts performance. - **Even with perfect alignment**, prompts provide an intrinsic benefit: full ColBERT pre-training with prompts (55.43) vs. without prompts (54.61), no mismatch in either case, shows a meaningful 0.82-point gap. **Why do prompts help?** Our leading hypothesis is that prompt tokens act as **implicit query expansion**: extra slots that don't carry specific meaning but let the model store global information about the sequence. The original ColBERT used `[PAD]` tokens for this purpose, but modern Flash Attention implementations broke this trick (masked tokens no longer produce usable embeddings). Explicit prompt tokens may be quietly re-enabling it.
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View details ↗colbertv2.0
lightonaiAll the credits belong to the original authors and we thank Omar Khattab for allowing us to share this version of the model. Please refer to the original repository and paper for more information about the model and to PyLate repository for information about usage of the model. 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 ↗DenseOn
lightonai> 🎯 **TL;DR**: A 149M-parameter dense (single-vector) retrieval model achieving **56.20 nDCG@10 on BEIR**, the first sub-150M dense model to break the 56 mark, **topping all base-size dense models** and outperforming several models **4× larger**. State-of-the-art retrieval is increasingly dominated by closed models, either hidden behind APIs or trained on undisclosed data. This blocks reproducibility, prevents study of possible data leakage, and gatekeeps progress to a handful of private labs. We thus decided to gather and curate a large amount of data and explore various mixtures. We release all the data used in our explorations: - Gathered pre-training data, 1.4B query-documents pairs alongside annotations used for non-destructive filtering (structural filtering, deduplication, cross-encoder pair relevancy) - Best pre-training mixture found with already applied filters - Fine-tuning datasets with query, positive and 2048 mined documents alongside their scores for 1.88M samples. Based on our findings, we trained LateOn (multi-vector/ColBERT) and DenseOn (single vector/dense) models on a proprietary Apache 2.0-compatible training dataset and release those models as well. Both are built on the ModernBERT backbone at 149M parameters, a size we believe sits at the sweet spot: large enough to handle real-world queries and documents, small enough to serve at high throughput in latency-sensitive production systems. For more information, please read our blogpost.
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View details ↗GTE-ModernColBERT-v1
lightonai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 ↗LateOn
lightonai📚 Collection | 📝 Blog State-of-the-art retrieval is increasingly dominated by closed models, either hidden behind APIs or trained on undisclosed data. This blocks reproducibility, prevents study of possible data leakage, and gatekeeps progress to a handful of private labs. We thus decided to gather and curate a large amount of data and explore various mixtures. We release all the data used in our explorations: - Gathered pre-training data, 1.4B query-documents pairs alongside annotations used for non-destructive filtering (structural filtering, deduplication, cross-encoder pair relevancy) - Best pre-training mixture found with already applied filters - Fine-tuning datasets with query, positive and 2048 mined documents alongside their scores for 1.88M samples. Based on our findings, we trained LateOn (multi-vector/ColBERT) and DenseOn (single vector/dense) models on a proprietary Apache 2.0-compatible training dataset and release those models as well. Both are built on the ModernBERT backbone at 149M parameters, a size we believe sits at the sweet spot: large enough to handle real-world queries and documents, small enough to serve at high throughput in latency-sensitive production systems. For more information, please read our blogpost.
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View details ↗LateOn-Code
lightonai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 ↗LateOn-Code-edge
lightonaiThe LateOn-Code collection is composed of PyLate models optimized for code retrieval. These late interaction models are first pre-trained following the methodology of CoRNStack. These pre-trained models are then further fine-tuned on train sets of CoIR using the nv-retriever methodology to mine hard negatives while preventing false negatives. We started from the two best ColBERT models on the BEIR benchmark for their respective sizes. The first one, LateOn-Code is based on in-house LateOn model, a new version of GTE-ModernColBERT-v1 built on ModernBERT-base (also developed at LightOn). This version underwent significantly deeper training, crossing the 57 mark on BEIR, almost a 2.5-point improvement and is thus SOTA by a large margin. We'll release this base model along with training data and boilerplates in the near future, so stay tuned\! The second, LateOn-Code-edge is a smaller model based on the edge-colbert model family from mixedbread, using the smallest variant (Ettin-17M) for maximum efficiency. For more details on the training setup, please refer to our blogpost. The original CoRNStack data in a format compatible with PyLate can be found here while the fine-tuning data can be found here. Training boilerplates can be found here in the PyLate repository
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View details ↗LateOn-unsupervised
lightonai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 ↗mDenseOn
lightonai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 ↗mDenseOn-unsupervised
lightonai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 ↗mLateOn
lightonai> 🎯 **TL;DR**: A 307M-parameter multilingual ColBERT (multi-vector) retrieval model achieving state-of-the-art results across **multilingual retrieval (MIRACL), long-document retrieval (MLDR), English general-domain retrieval (BEIR), and code retrieval (MTEB Code)**. Trained on nine languages only, yet generalizes to unseen languages and scripts, demonstrating that **late interaction enables translate-train viability** for broad multilingual coverage without exhaustive translation. With DenseOn and LateOn, we demonstrated that an open, carefully curated data recipe can match closed-data retrieval models on English. mDenseOn and mLateOn extend this recipe to multilingual, long-context, and code retrieval. Rather than independently collecting multilingual corpora from scratch (which would be expensive, uneven across languages, and hard to curate at the same quality), we applied the **translate-train** approach: machine-translating our validated English data into eight target languages (French, German, Italian, Spanish, Portuguese, Swedish, Norwegian, and Arabic) and adding cross-lingual pairs for cross-lingual alignment.
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View details ↗modernbert-embed-large
lightonai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 ↗modernbert-embed-large-unsupervised
lightonai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 ↗mSlideOn
lightonai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 ↗Reason-ModernColBERT
lightonaiReason-ModernColBERT is a late interaction model trained on the reasonir-hq dataset. It achieves extremely competitive performance on the BRIGHT benchmark aimed at evaluating reasoning-intensive retrieval performance, outperforming all existing models up to 7B (more than 45 times its size) and even surprisingly improving performance of ReasonIR-8B (a 8B model trained on the same data) by more than 2.5 NDCG@10 on average on Stack Exchange splits. We attribute such strong results to late-interaction, see evaluation section. Unfortunately, since the ReasonIR data has been released under a cc-by-nc-4.0 license, we cannot release this model under an Apache 2.0 license. However, the authors of ReasonIR released code to generate the data. Anyone willing to reproduce the data could then easily reproduce this model under an Apache 2.0 license by running a fine-tuning lasting lower than 2 hours using this boilerplate. This is a multi-vector (ColBERT-style late interaction) embedding model finetuned from lightonai/GTE-ModernColBERT-v1 on the reasonir-hq dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
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View details ↗SlideOn
lightonai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 ↗clip-japanese-base
line-corporationThis is a Japanese CLIP (Contrastive Language-Image Pre-training) model developed by LY Corporation. This model was trained on ~1B web-collected image-text pairs, and it is applicable to various visual tasks including zero-shot image classification, text-to-image or image-to-text retrieval. The model uses an Eva02-B Transformer architecture as the image encoder and a 12-layer BERT as the text encoder. The text encoder was initialized from rinna/japanese-clip-vit-b-16. - STAIR Captions (v2014 val set of MSCOCO) for image-to-text (i2t) and text-to-image (t2i) retrieval. We measure performance using R@1, which is the average recall of i2t and t2i retrieval. - Recruit Datasets for image classification. - ImageNet-1K for image classification. We translated all classnames into Japanese. The classnames and templates can be found in ja-imagenet-1k-classnames.txt and ja-imagenet-1k-templates.txt.
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