Apertus-70B-2509
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Apertus-70B-Instruct-2509
swiss-aiApertus is a 70B and 8B parameter language model designed to push the boundaries of fully-open multilingual and transparent models. The model supports over 1000 languages and long context, it uses only fully compliant and open training data, and achieves comparable performance to models trained behind closed doors. The model is a decoder-only transformer, pretrained on 15T tokens with a staged curriculum of web, code and math data. The model uses a new xIELU activation function and is trained from scratch with the AdEMAMix optimizer. Post-training included supervised fine-tuning and alignment via QRPO. The modeling code for Apertus is available in transformers `v4.56.0` and later, so make sure to upgrade your transformers version. You can also load the model with the latest `vLLM` which uses transformers as a backend.
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View details ↗Apertus-8B-2509
swiss-aiApertus is a 70B and 8B parameter language model designed to push the boundaries of fully-open multilingual and transparent models. The model supports over 1000 languages and long context, it uses only fully compliant and open training data, and achieves comparable performance to models trained behind closed doors. The model is a decoder-only transformer, pretrained on 15T tokens with a staged curriculum of web, code and math data. The model uses a new xIELU activation function and is trained from scratch with the AdEMAMix optimizer. Post-training included supervised fine-tuning and alignment via QRPO. The modeling code for Apertus is available in transformers `v4.56.0` and later, so make sure to upgrade your transformers version. You can also load the model with the latest `vLLM` which uses transformers as a backend.
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View details ↗Apertus-8B-Instruct-2509
swiss-ai1. Model Summary 2. How to use 3. Evaluation 4. Training 5. Limitations 6. Legal Aspects Apertus is a 70B and 8B parameter language model designed to push the boundaries of fully-open multilingual and transparent models. The model supports over 1000 languages and long context, it uses only fully compliant and open training data, and achieves comparable performance to models trained behind closed doors. The model is a decoder-only transformer, pretrained on 15T tokens with a staged curriculum of web, code and math data. The model uses a new xIELU activation function and is trained from scratch with the AdEMAMix optimizer. Post-training included supervised fine-tuning and alignment via QRPO.
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View details ↗Apertus-v1.1-0.5B
swiss-aiApertus-v1.1 is a series of highly efficient, 0.5-4B billion parameter language models designed to extend the fully-open and compliant Apertus ecosystem to highly constrained hardware environments. The models rely on a dense transformer architecture featuring grouped-query attention and xIELU activations. To achieve high performance with a minimized memory footprint, this model uses tied embeddings and a deeper, thinner architectural design. Instead of standard pre-training, Apertus-v1.1 models were created using pre-training distillation (PD) from the Apertus-8B-2509 teacher model. They were trained on 1.7T tokens from Phase 5 of the original Apertus data pipeline—the highest quality tier of filtered documents, code, and instruction samples without introducing any new data sources or licenses.
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View details ↗Apertus-v1.1-0.5B-Instruct
swiss-ai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 ↗Apertus-v1.1-1.5B
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View details ↗Apertus-v1.1-1.5B-Instruct
swiss-aiApertus-v1.1 is a series of highly efficient, 0.5-4B billion parameter language models designed to extend the fully-open and compliant Apertus ecosystem to highly constrained hardware environments. The models rely on a dense transformer architecture featuring grouped-query attention and xIELU activations. Instead of standard pre-training, Apertus-v1.1 models were created using pre-training distillation (PD) from the Apertus-8B-2509 teacher model. They were trained on 1.7T tokens from Phase 5 of the original Apertus data pipeline—the highest quality tier of filtered documents, code, and instruction samples without introducing any new data sources or licenses. Post-training included supervised fine-tuning (SFT) and alignment similar to that of the original Apertus.
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View details ↗Apertus-v1.1-4B
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View details ↗Apertus-v1.1-4B-Instruct
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View details ↗Apertus-v1.5-70B
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View details ↗Apertus-v1.5-70B-base
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View details ↗Apertus-v1.5-8B
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View details ↗Apertus-v1.5-8B-base
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View details ↗Apertus-v1.5-8B-SFT
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