VTP-Large-f16d64
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AIモデルを探す 85001
VTP-Small-f16d64
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詳細を見る ↗M2V_base_glove
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詳細を見る ↗M2V_base_glove_subword
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詳細を見る ↗M2V_base_output
minishlabThis Model2Vec model is a distilled version of the baai/bge-base-en-v1.5 Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec. It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using zipf weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence.
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詳細を見る ↗M2V_multilingual_output
minishlabThis Model2Vec model is a distilled version of the sentence-transformers/LaBSE Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec. It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using zipf weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence.
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詳細を見る ↗potion-base-2M
minishlabThis Model2Vec model is pre-trained using Tokenlearn. It is a distilled version of the baai/bge-base-en-v1.5 Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2vec creates a small, static model that outperforms other static embedding models by a large margin on all tasks on MTEB. This model is pre-trained using Tokenlearn. It's created using the following steps: - Distillation: first, a model is distilled from a sentence transformer model using Model2Vec. - Training data creation: the sentence transformer model is used to create training data by creating mean output embeddings on a large corpus. - Training: the distilled model is trained on the training data using Tokenlearn. - Post-training re-regularization: after training, the model is re-regularized by weighting the tokens based on their frequency, applying PCA, and finally applying SIF weighting. The results for this model can be found on the Model2Vec results page.
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詳細を見る ↗potion-base-32M
minishlabThis Model2Vec model is pre-trained using Tokenlearn. It is a distilled version of the baai/bge-base-en-v1.5 Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. It uses a larger vocabulary size than the potion-base-8M model which can be beneficial for tasks that require a larger vocabulary. Model2vec creates a small, static model that outperforms other static embedding models by a large margin on all tasks on MTEB. This model is pre-trained using Tokenlearn. It's created using the following steps: - Distillation: first, a model is distilled from a sentence transformer model using Model2Vec. - Training data creation: the sentence transformer model is used to create training data by creating mean output embeddings on a large corpus. - Training: the distilled model is trained on the training data using Tokenlearn. - Post-training re-regularization: after training, the model is re-regularized by weighting the tokens based on their frequency, applying PCA, and finally applying SIF weighting. The results show that **potion-base-32M** is the most performant static embedding model, reaching 94.66% of the performance of all-MiniLM-L6-v2 with an average score of 52.83 while being orders of magnitude faster.
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詳細を見る ↗potion-base-4M
minishlabThis Model2Vec model is pre-trained using Tokenlearn. It is a distilled version of the baai/bge-base-en-v1.5 Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2vec creates a small, static model that outperforms other static embedding models by a large margin on all tasks on MTEB. This model is pre-trained using Tokenlearn. It's created using the following steps: - Distillation: first, a model is distilled from a sentence transformer model using Model2Vec. - Training data creation: the sentence transformer model is used to create training data by creating mean output embeddings on a large corpus. - Training: the distilled model is trained on the training data using Tokenlearn. - Post-training re-regularization: after training, the model is re-regularized by weighting the tokens based on their frequency, applying PCA, and finally applying SIF weighting. The results show that **potion-base-4M** reaches 89.63% of the performance of all-MiniLM-L6-v2 with an average score of 50.01 while being orders of magnitude faster.
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詳細を見る ↗potion-base-8M
minishlabThis Model2Vec model is pre-trained using Tokenlearn. It is a distilled version of the baai/bge-base-en-v1.5 Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2vec creates a small, static model that outperforms other static embedding models by a large margin on all tasks on MTEB. This model is pre-trained using Tokenlearn. It's created using the following steps: - Distillation: first, a model is distilled from a sentence transformer model using Model2Vec. - Training data creation: the sentence transformer model is used to create training data by creating mean output embeddings on a large corpus. - Training: the distilled model is trained on the training data using Tokenlearn. - Post-training re-regularization: after training, the model is re-regularized by weighting the tokens based on their frequency, applying PCA, and finally applying SIF weighting. The results for this model can be found on the Model2Vec results page.
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詳細を見る ↗potion-code-16M
minishlab**This model is superseded by potion-code-16M-v2. We recommend using that instead for better performance at the same size.** **potion-code-16M** is a fast static code embedding model optimized for code retrieval tasks. It powers Semble, a code search library for agents. It is distilled from nomic-ai/CodeRankEmbed and trained on the CornStack code corpus using Tokenlearn and contrastive fine-tuning. It uses static embeddings, allowing text and code embeddings to be computed orders of magnitude faster than transformer-based models on both GPU and CPU.
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詳細を見る ↗potion-code-16M-v2
minishlab**potion-code-16M-v2** is a fast static code embedding model optimized for code retrieval tasks. It powers Semble, a code search library for agents. It is distilled from nomic-ai/CodeRankEmbed and trained on the CornStack code corpus using Tokenlearn and contrastive fine-tuning. It is the successor to potion-code-16M. It uses static embeddings, allowing text and code embeddings to be computed orders of magnitude faster than transformer-based models on both GPU and CPU. 1. **Vocabulary mining**: code-specific tokens are mined from CornStack and added to the base CodeRankEmbed tokenizer (43k extra tokens → ~63.5k total) 2. **Distillation**: the extended vocabulary is distilled from CodeRankEmbed using Model2Vec (256-dimensional embeddings, PCA) 3. **Tokenlearn**: the distilled model is fine-tuned on 1.2 million (query, document) pairs from CornStack using cosine similarity loss 4. **Contrastive fine-tuning**: the model is further fine-tuned using MultipleNegativesRankingLoss on 1.2 million CornStack query-document pairs Results on the CoIR benchmark on MTEB (NDCG@10, `mteb>=2.10`):
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詳細を見る ↗potion-multilingual-128M
minishlabThis Model2Vec model is pre-trained using Tokenlearn on all languages in the C4 dataset. It is a distilled version of the BAAI/bge-m3 Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. potion-multilingual-128M is a multilingual model, trained on 101 languages, and is capable of generating embeddings for any text in any language. The model produces 256 dimensional embeddings, and has a theoretically unlimited context length since embeddings are static (pre-computed). The Model2Vec library is the fastest and most lightweight way to run Model2Vec models.
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詳細を見る ↗potion-multilingual-128m-onnx
minishlabThis is an ONNX export of the minishlab/potion-multilingual-128m Model2Vec model, produced with the ONNX runtime. Model2Vec models use static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. This ONNX export lets you run the model with `onnxruntime` or `transformers.js`, without depending on the `model2vec` package. If you don't need the ONNX runtime, you can load the original model with the Model2Vec library instead:
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詳細を見る ↗potion-retrieval-32M
minishlabThis Model2Vec model is optmized for retrieval tasks. It is a finetune of potion-base-32M. It's finetuned using a modified version of the training approach described in this blogpost. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2vec creates a small, static model that outperforms other static embedding models by a large margin on all tasks on MTEB. This model is pre-trained using Tokenlearn. It's created using the following steps: - Distillation: first, a model is distilled from a sentence transformer model using Model2Vec. - Training data creation: the sentence transformer model is used to create training data by creating mean output embeddings on a large corpus. - Training: the distilled model is trained on the training data using Tokenlearn. - Post-training re-regularization: after training, the model is re-regularized by weighting the tokens based on their frequency, applying PCA, and finally applying SIF weighting. The results for this model can be found on the Model2Vec results page.
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詳細を見る ↗potion-retrieval-32m-onnx
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詳細を見る ↗potion-science-32M
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詳細を見る ↗Ministral-3b-instruct
ministralMinistral is a series of language model, build with same architecture as the famous Mistral model, but with less size. - **Model type:** A 3B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - **Language(s) (NLP):** Primarily English - **License:** Apache 2.0 - **Finetuned from model:** mistralai/Mistral-7B-v0.1
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詳細を見る ↗Ministral-4b-instruct
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詳細を見る ↗qwen3p6-35b-a3b-2p02bit-r56
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詳細を見る ↗qwen3p6-35b-a3b-2p02bit-r68
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詳細を見る ↗qwen3p6-35b-a3b-2p02bit-r69
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