Agent-ModernColBERT
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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 ↗colgrep-agent-2B
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View details ↗colgrep-agent-2B-GGUF
lightonai--- library_name: transformers base_model: openbmb/MiniCPM5-2B pipeline_tag: text-generation tags: - code - code-search - tool-use - reinforcement-learning - colgrep language: - en license: apache-2.0 --- **ColGrep-Agent-2B** is a lightweight code-localization agent designed to identify the files and line ranges relevant to a GitHub issue, bug report, feature request, or natural-language question about a repository. Given a question such as *"Where are retries configured?"* or *"Why does the export crash on empty files?"*, the agent iteratively searches the repository using ColGrep, reads candidate files through a read-only terminal, and returns the precise code locations relevant to the task.
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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 ↗DSE_Qwen_2.5_SOTA
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View details ↗GTE-ModernColBERT-v1
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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
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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
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View details ↗LightOn-rerank-LW-2B
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View details ↗LightOn-rerank-LW-4B
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View details ↗LightOn-rerank-PW-2B
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View details ↗LightOn-rerank-PW-4B
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View details ↗LightOnOCR-0.9B-16k-1025
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View details ↗LightOnOCR-0.9B-32k-1025
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View details ↗LightOnOCR-1B-1025
lightonai> [!NOTE] > 🚀 **LightOnOCR-2 is now available and state-of-the-art on OlmOCR-bench**, with new image detection variants! > Check it out here: **lightonai/LightOnOCR-2-1B** Full BF16 version of the model. We recommend this variant for inference and further fine-tuning. **LightOnOCR-1B** is a compact, end-to-end vision–language model for Optical Character Recognition (OCR) and document understanding. It achieves state-of-the-art accuracy in its weight class while being several times faster and cheaper than larger general-purpose VLMs.
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View details ↗LightOnOCR-2-1B
lightonai📄 Paper | 📝 Blog | 🚀 Demo | 📊 Dataset | 📓 Finetuning **Best OCR model .** LightOnOCR-2-1B is **LightOn's** flagship OCR model, refined with RLVR training for maximum accuracy. We recommend this variant for most OCR tasks. LightOnOCR-2 is an efficient end-to-end 1B-parameter vision-language model for converting documents (PDFs, scans, images) into clean, naturally ordered text without relying on brittle pipelines. This second version is trained on a larger and higher-quality corpus with stronger French, arXiv, and scan coverage, improved LaTeX handling, and cleaner normalization. LightOnOCR-2 achieves state-of-the-art performance on OlmOCR-Bench while being ~9× smaller and significantly faster than competing approaches.
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View details ↗LightOnOCR-2-1B-base
lightonai**Base model for fine-tuning.** This is the pre-RLVR checkpoint with strong OCR capabilities, ideal as a starting point for domain adaptation and custom fine-tuning. LightOnOCR-2 is an efficient end-to-end 1B-parameter vision-language model for converting documents (PDFs, scans, images) into clean, naturally ordered text without relying on brittle pipelines. This second version is trained on a larger and higher-quality corpus with stronger French, arXiv, and scan coverage, improved LaTeX handling, and cleaner normalization. LightOnOCR-2 achieves state-of-the-art performance on OlmOCR-Bench while being ~9× smaller and significantly faster than competing approaches.
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View details ↗LightOnOCR-2-1B-bbox
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View details ↗LightOnOCR-2-1B-bbox-soup
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