Apertus 1.5 Release Notes

來源原標題

Apertus 1.5 Release Notes

機構

HuggingFace

原始連結

https://huggingface.co/blog/swiss-ai/apertus-v15-release-notes

摘要原文

Back to Articles Apertus 1.5 Release Notes Team Article Published October 15, 2026 Upvote 29 +23 Hanna Yukhymenko hannayukhymenko Follow swiss-ai Oleg Lavrovsky loleg Follow swiss-ai Y Metz ymetz Follow swiss-ai Our latest release, Apertus 1.5, extends the 8B and 70B Apertus 1.0 models with image/audio understanding, an optional thinking mode, a four times longer context window, improved instruction-following, and better tool use. The updated 8B and 70B model weights were released to Hugging Face on July 24, 2026 under an Apache 2.0 license. Further details are now available from the project Technical Report , and summarized here. Apertus 1.5 is a continued pretraining of the first generation model: adding 4 trillion tokens of text and multimodal training data to the 8B model and 2 trillion tokens to the 70B model, for a total of 19T and 17T, respectively. As with every Apertus release, the models are fully open: open weights , open data , open values , and full training details . Key Enhancements Native Multimodal Understanding: Apertus 1.5 accepts images and audio alongside text, bringing documents, diagrams, and photos into the conversation. The models can also process spoken language, though this remains experimental. Thinking Mode: The models can be switched into a thinking mode to reason about the input before answering, which improves performance on reasoning problems. Long Context: The context window grows to 262,144 tokens, a 4x increase over Apertus 1.0. Improved Instruction-Following: Better instruction adherence than Apertus 1.0 gives more predictable and accurate responses. Improved Tool Use: The models are trained for better tool integration, allowing more effective use of external tools and APIs. Open Values: Stronger adherence to the Apertus Charter, which brings transparency to the model’s values and principles. Training Process The full Apertus 1.5 training pipeline Multimodality Through the Tokenizer We introduce multimodality in v1.5 models in an early-fusion manner, where images and audio enter Apertus as discrete tokens in the same sequence with text tokens, instead of using a separate vision encoder. Images are tokenized with the Emu3.5 tokenizer (a 512×512 image becomes 1,024 tokens) and audio with WavTokenizer (40 tokens per second). We only extend the vocabulary, so the architecture stays unchanged and training runs at text-only speed. The models understand images and audio but generate only text. Pre-Training Starting from Apertus 1.0, we continue pretraining in four stages: Unpaired: text plus standalone images and audio, so the model learns the new modalities. Paired: image–caption and audio–transcript pairs, so it aligns them with language. Cooldown: higher-quality data with more math and code. Long context: the window grows step by step from 8k to 262k tokens. Overall, more than half of the training tokens are text-only, which preserves the language abilities of Apertus 1.0. The text data is refreshed with sources such as FineWeb2-HQ+ , FinePDFs, and Nemotron-CC. All new data, multimodal included, meets the same bar as before: publicly available, permissively licensed, and with robots.txt opt-outs respected retroactively. Every source is listed in apertus-data-transparency . Post-Training Post-training runs in five stages: SFT → long-context SFT → RL → DPO → charter alignment (SDPO) . SFT on 3.7M text samples plus image and speech data. Reasoning traces are added only to prompts the model can't already solve without them. Tool-use data is restricted to execution-verified trajectories. We release the text SFT mix we used on Hugging Face. Long-context SFT at the full 262k window restores the long-context ability that short-context SFT erodes. RL with verifiable rewards (RLVR) on about 350k math, code, instruction-following, multilingual, and vision prompts, mixing thinking on and off. DPO with judge-scored preference pairs improves chat quality, tool use, and safety. We release the preference pool and training pairs . SDPO aligns the model with the Apertus Charter through judge feedback and self-distillation. Evaluation Results Alongside the extended model capabilities, we also significantly expanded the scope of evaluations. Beyond the existing text-based evaluations used for Apertus 1.0, we added multimodal evaluations (image & audio), and thinking-mode evaluations. The multimodal evaluations are done through MLLM-eval-suite pipeline, merging two major multimodal evaluation frameworks lmms-eval and VLMEvalKit . We also added a series of text-based benchmarks, as well as extended benchmarking for translation and multilingual capabilities, safety, and tool use. These can be found in our post-training evaluation repository , based on lm-evaluation-harness fork . While still trailing frontier open-weight models, the new release has also significantly improved performance in coding and instruction following capabilities with Apertus 1.5 8B outperforming similarly sized open-weight models on IF and multilingual tasks. Thinking further improves performance on challenging reasoning-heavy benchmarks like AIME. The multimodal performance of the model is closer to Gemma 4 12B and other current open-weight models. Apertus 1.5 multimodal performance is competitive in spatial understanding among open-weight models and document/chart understanding among open-source models. We also thank our collaborators and external evaluators for their help with efficient benchmarking of Apertus capabilities. In particular, Apertus 1.5 shows strong performance on Swiss Legal tasks, being on par with leading open-weight models ( blog , code ). Additionally, on Last Translation Benchmark, Apertus 1.5 8B presents machine translation capabilities which match GPT-OSS-20B and outperform models like Claude Haiku 4.5 or Google Translate ( leaderboard , paper ). Evaluation Cards: Apertus ❤️ EvalEval Apertus collaborates with the EvalEval Coalition to ensure fully transparent and reproducible benchmarking. We publish the Apertus 1.5 results in the Every Eval Ever (EEE) schema, a shared format that records not only scores, but also who ran the evaluation, how the model was accessed, the generation settings, and how each metric should be interpreted: details that are routinely missing from leaderboards and that often explain why similar-looking scores differ. The results are shown in the Eval Card below, which combines the run data with benchmark and model metadata into one interpretable record, and are also available through Hugging Face Community Evals . This is the same infrastructure the UK AI Security Institute uses to publish its evaluations openly . Since our text evaluations run on our public fork of lm-evaluation-harness, which EEE supports with a dedicated converter, anyone can rerun them and compare their numbers against ours under identical settings. Structured evaluation reporting also matters beyond research. EvalEval has mapped Evaluation Cards to AI governance requirements , including Article 55 of the EU AI Act and the GPAI Code of Practice, and found that the EEE schema can represent 56 of the 60 evaluation-reporting elements these frameworks call for. This fits how we already release Apertus: alongside the models, we publish a Public Summary of Training Content and, voluntarily, a Code of Practice document. Machine-readable evaluation records are a natural next step: making it easier for regulators, deployers, and researchers to find, interpret, and compare the evidence behind a model. We are committed to promoting reproducible and transparent evaluation through Eval Cards and the EEE schema, and encourage other model developers to report their results in the same way, so that scores can be compared across models and organizations. Apertus 1.5 Reproducibility All recipes, ablations, and data mixtures are documented in the Technical Report . We aim to popularize open model development by making the v1.