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Model Card Luminous

This model card presents information for all foundation models of the Luminous family. It allows for easy comparison of different model sizes. Unless explicitly stated, the information presented here applies universally to all Luminous models.

Please note that this model card describes the foundational large language models of the Luminous family: fine-tuned versions of the models, including extensions (e.g., Luminous-control or Luminous-explore), are not the focus of this model card.

Model Details

Model Description

  • Developed by: Aleph Alpha GmbH
  • Model type/architecture: Autoregressive (causal, decoder only) transformer language model with rotary position embeddings and are trained on the next token prediction task. Luminous models are standalone transformer foundation models with the intention to be integrated in broader AI applications (systems).
  • Language(s) (NLP): English, German, French, Spanish, Italian
  • Multimodality: Luminous-base and Luminous-extended provide multi-modal input capabilities (a prompt may consist of any combination of images and text). Multi-modal capabilities extend the number of parameters. All available luminous models are trained to output text only. All architectural changes are detailed in the MAGMA paper.
  • Model versions: The API will serve the model versions described in this model card until further notice. No restrictions, customizations or specializations are applied by any means. The same models are made available to users regardless of their country, geographic location, and the input language but subject to sanction regimes, technology export regulations, and other restrictions that may apply. In effect the same offering is provided to all countries within and external to the European Union if no legal restrictions apply.
ModelParameter countDescription
Luminous-base~13B (~15B with multi-modality)Luminous-base is our smallest model. That makes it our fastest and cheapest model. Therefore, it is best used for use-cases where speed is important and costs should be low. This may include tasks like classification and labelling.
Luminous-extended~30B (~42B with multi-modality)Luminous-extended is our second-largest model. It is well suited for tasks like information extraction and language simplification. It performs better on a wide range of tasks compared to Luminous-base and is faster and cheaper than Luminous-supreme.
Luminous-supreme~70BLuminous-supreme is the largest and most capable model in the Luminous model family. It can solve all the tasks that the smaller models can solve and is particularly suited for creative text writing.
Luminous-control modelsDepends on underlying vanilla modelAll three vanilla models are available as control versions. These models variants are optimized to follow instructions. They have been fine-tuned on a diverse set of text-based tasks & use-cases. As a result, they come with much improved zero-shot performance. This makes them easier to use. Control versions are available for base, extended, and supreme.

Model Access

  • API: Each model can be accessed using our public API (after registration and acceptance of our Terms of Use). Clients are available for python and rust. Please refer to the documentation for code snippets and examples.
  • Playground: The playground provides a UI for quick model interaction (after registration and acceptance of our Terms of Use). The playground is intended for research only and not optimized for chat usage.
  • On premise installation or AI-As-A-Service: Contact us for options to deploy the Luminous models in your environment. We grant on-prem customers of Aleph Alpha open access to our full model checkpoint including weights and code.

Please refer to the changelog for updates to the models served and the API interface. We do not deprecate old model versions when we release newer versions, meaning that users can maintain access to the available models.

No prompt data is stored when using the API or playground, which means that we do not collect PII for any of our API users as detailed in our Terms & conditions. We do not log user inputs to the models. We do not train on user data.

Model Creator

We have leveraged human labor solely via employees employed at Aleph Alpha in Germany (EU), subject to and honoring all employment rights of Germany, including but not limited to German minimum wage and non-discriminatory stipulations, across the full data pipeline for activities related to data collection, annotation, filtering, and validation for all data segments mentioned below.

Outside of data labor, we have collaborated with academic research partners at TU Darmstadt and University of Heidelberg, resulting in published peer-reviewed papers.

Model Release

The steps required to release a model consist of rigorous oversight by our Research Committee at each of the model development stages listed below. This includes a review of the pre-training run and pre-determined training objectives, followed by an extensive internal evaluation of the Aleph Alpha benchmark suit. After all modifications and mitigations to the model have been decided and enforced by the Research Committee, the model's behavior is reviewed under our ethics framework to ensure that the model complies with relevant laws, regulations and ethical guidelines. The model is then released to Aleph Alpha's business partners. After a successful trial period, the model is released on our API.


Direct Use

Luminous models are intended to be deployed as AI modules in an ecosystem of components. They are built to be called with well designed prompts. A plain model is unlikely to respond in a manner that is anticipated for a given use case.

Downstream Use

Use cases and the model's capabilities include but are not limited to:

  • Text generation
  • Classification
  • Summarization
  • Question answering
  • Brainstorming
  • Labeling
  • etc.

Examples for prompts can be found in the playground. Use-case examples are laid out here. Explore different ways of calling Luminous here.

Out-of-Scope Use and Limitations

The models are not to be used for illegal actions of any kind. This includes compliance with sanction regimes, technology export regulations, and other restrictions that may apply. They are to be used following ethical standards. The utilization of our technology is always governed by, and may be limited in accordance with, our Terms of Use or any specific agreement we might have established with you.

Although we do not inspect the requests sent to our API/Playground and therefore cannot verify compliance with our Usage Policy, the Aleph Alpha team regularly looks for publicly known problems and violations that may be related to our models and takes legal action against them. This includes, but is not limited to, enforcement to remove published model content, compensation for damages caused, and account termination or removal of credits. We provide justification along with our enforcement actions. In addition, we use a whistleblowing solution in accordance with the Whistleblower Protection Act (HinSchG), which helps users and affected parties to report usage that violates our policies, which in turn will be prosecuted.

For non-anonymous reports, we also provide an appeals / claims mechanism for usage policy violations via our dedicated contact address to communicate with us.

Customers and partners are enabled to use our ticketing system for appeals, claims and feedback.

To date, there have not been any government inquiries related to the model for content to be banned, requests for information about a developer's business practices, or the like.

Training Details

Training Data

The Luminous family models have been developed using a diverse dataset that primarily includes English, German, French, Spanish, and Italian content. While the dataset may contain other languages, the performance of the Luminous models on these languages has not been evaluated, and as a result, performance may be reduced. The dataset was curated to ensure a balanced representation of various languages across a range of styles and formats, focusing on quality of sources (minimal duplicates and artifacts, text coherence), diversity (spanning styles, world knowledge and domains), relevance to our customers/domain (language mix, domain-specific knowledge), and source availability/cost to obtain. The pre-training phase for the Luminous models was conducted entirely through self-supervision, without the use of any supervised datasets. For more specialized applications, please refer to the Luminous-*-control and Luminous-explore models. It's important to note that computer code has been intentionally excluded from the training dataset.

The following tables provides a summarization of included training data.

DatasetDescriptionPercentageTotal Size (Tokenized)Tokens
Web CrawlsLarge web scrape corpora (e.g. Common Crawl) containing various styles and sources71%2,77TB761,41B
BooksFiction and non-fiction literature providing well-structured and coherent text on various topics20%0,79TB217,15B
Political and Legal SourcesData provided by the EU parliament, legislation and speeches5%0,18TB49.47B
WikipediaWikipedia provides well-structured and mostly factual information2%0,07TB19,29B
NewsNews articles from various journals2%0,06TB16,49B
OtherCollection of smaller, more specialized datasets (e.g. philosophy texts)1%0,02TB5,49B

All Luminous models were trained using the same data seed (order and mix), although each model has seen a different number of tokens. Data sources have not been augmented nor have we generated any synthetic data for any of our luminous models. No further curation beyond the data sources and filtering mentioned in the pre-processing has been performed, thereby ensuring the integrity of each data source. Therefore, the source data represents an unbiased demographic distribution of global authors fluent in the selected languages.

Given the amount of data and the variability in use cases, there is the possibility that the training data contains undesirable content (which cannot be excluded entirely). Undesirable content may refer to anything hateful, harmful, factually wrong or otherwise detrimental to the use case at hand. Please refer to the Bias, Risks, and Limitations-section for more information and recommendations to reduce bias.

As Aleph Alpha, we acknowledge and abide by copyright and related legislation. Text and data mining is performed in full compliance with such legislation, including Article 4 of Directive (EU) 2019/790 (adopted by Germany in Sec. 44b German Copyright Act (Urheberrechtsgesetz)) and its provisions on reservations by rightholders in an appropriate manner, such as machine-readable means in the case of content made publicly available online.

Data Access

We adhere to applicable legislation, including but not limited to the German Copyright Act ("Urheberrechtsgesetz").

This includes that copyright protected works, if used for AI training, are to be deleted when they are no longer required for the purposes exempted under such law. Statutory copyright exemptions do not provide for or allow making available or distribution of such works to external parties, including queryable external data access.


The data underwent a cleaning process, which involved filtering out low-quality and unintended content to ensure its overall integrity and relevance.

Filtering: Source data has been filtered according to established language classifiers and corresponding scores, as well as Aleph Alpha’s own supervised-trained quality classifiers as well as selected among structured datasets. The Luminous quality classifiers has the following properties:

  • Classifiers were trained to identify undesired text types, such as non-readable text
  • For each language, we trained a separate classifier

We have not filtered training data beyond this score-based and quality-based filtering.

Tokenization: The Luminous tokenizer is a learned subword tokenizer and has the following properties:

  • We use the byte-level Byte Pair Encoding (BPE) algorithm
  • We apply NFC normalization
  • A prefix whitespace is added
  • Vocabulary size of 128000
  • Tokens were learnt from a language-balanced sample of the training data

Text can be tokenized and detokenized via the API, and locally. Please refer to the related documentation for examples.


The most resource-intensive step in the development of the Luminous base models was their self-supervised training phase, using the diverse dataset described above and the PyTorch framework as the training environment, which is the only core dependency. After random initialization of all parameters, the model was trained to predict the next token in a sequence, minimizing cross-entropy loss, and stopped after a fixed number of iterations.


For the multimodal capabilities, the model includes a vision encoder. This component is trained to understand and encode visual information, which is then integrated with textual data for processing. The vision encoder is trained on a large-scale dataset of images and their associated textual descriptions. The training process is similar to the language pre-training, but the model is trained to predict the next token in a sequence of visual features.

Further multimodal training & architecture details can be found in the MAGMA paper.


To significantly improve the quality of the output, we fine-tuned the Luminous models on diverse and high-quality Instruction-Context-Output triples to create control models. Specifically this means the fine-tuning adapters on a next token prediction task with a loss mask on the expected completion, while keeping the base model parameters frozen.

With our control models, we mitigate model risks that are trained to address some of the risks and biases mentioned below.


We only used annotated data for supervised fine-tuning i.e., Luminous-control models.

For our supervised fine-tuning (luminous control models), we restricted data annotations to either source-available commercially usable data, or proprietary data that has been annotated by people based of EU and paid at least minimum wage. In order to comply with privacy regulations, and limit data to the bare minimum required to train performant models, all meta data, e.g., EXIF, and other potentially personalized information on the people who created the data has been removed in early stages of our data pipeline.

Training efficiency

The following table shows the number of train iterations with related token counts for the Luminous models. Efficiency is measured in TFlops in accordance with the Bloom implementation to provide comparability to the related paper. Train jobs on different topologies (different GPU counts) have been accounted for by a weighted average. Language pre-training used orders of magnitude more compute than subsequent multi-modal pre-training. Accordingly, the Luminous-base and -extended architectures were optimised to make efficient use of A100 GPUs during language pre-training. This leads to slightly lower efficiency for the multi-modal extensions, where the architecture is extended (see the MAGMA paper).

Model nameParameter countIterationsTraining tokensTFlops
Luminous-base multi-modal extension~15B60000~31B133
Luminous-extended multi-modal extension~42B100000~58B121


Luminous models were evaluated both as part of the Holistic Evaluation of Language Models (HELM) and by Aleph Alpha. The evaluations are made available in the form of a blog post and a pdf.

Aleph Alpha’s evaluations comprise accuracy based metrics. Next to accuracy based metrics, HELM also evaluates with regards to calibration, robustness, fairness, general information, bias, toxicity and summarization. Results can be viewed in direct comparison to comparable models.

Bias, Risks and Limitations

Harmful Language

Language models can sometimes generate outputs that are unsuitable for certain applications. This includes producing content with harmful language, inappropriate tone and style, systemic biases, or suggestions that encourage illegal actions. Such outputs can also include incorrect, outdated information, or material that is not suitable for all ages. To minimize these issues, the following strategies are recommended to be employed:

  • Crafting prompts carefully to guide the model's output more effectively.
  • Utilizing a finetuned model (often referred to as a control model) that prioritizes using explicitly provided information.
  • Employing a finetuned model designed to maintain an appropriate tone and style, including avoiding offensive language.
  • Implementing explainability checks to create an audit trail at the application level.
  • Conducting additional validations at the application level to ensure output quality and appropriateness.

Systematic Biases

Language models obtain world-knowledge from their pre-training data and may therefore exhibit the same systematic biases that are present in the data. Differing deployment scenarios (including differing cultural contexts) can expose systematic biases in differing ways. We acknowledge the cultural diversity of communities and users inside and outside the EU. We encourage users to track systematic biases relevant to their use-case, and we are happy to consult on bespoke fine-tunings to alleviate such biases.

Outdated World Knowledge

Pre-training was performed using a fixed dataset, created at a fixed date in the past. Accordingly, the world knowledge of foundation models is limited to the information contained in its training data. More recent information may not be known to the model or misunderstood when presented as input during live usage. Risks include:

  • Generation of personally identifiable information. Models are not trained to provide, but may seem to provide personally identifiable information. This does not necessarily imply the presence of such information in training data, as hallucination is possible.
  • Generation of unintended, irrelevant or repetitive outputs. This includes the production of incorrect or outdated information.

Risks may be mitigated by:

  • Injecting context, where relevant.
  • Crafting prompts carefully to guide the model's output more effectively.
  • Performing validations on the application layer (e.g., classifying the output).
  • Using the repetition penalty, especially in the case of repetition, or other parameters available in the API (see documentation).
  • Avoiding of use cases targeted at retrieval of personally identifiable information.

Political Bias

The Luminous family has not been optimized to represent a political opinion or take a specific point of view. It may generate outputs that contradict a user's opinion or expectation (e.g. produce hateful, violent or inappropriate, biased or discriminatory content). Such behaviour may be addressed by:

  • Crafting prompts carefully to guide the model's output more effectively.
  • Performing validations on the application layer (e.g., via Red-Teaming or classifying the output).

Mistaken for a Human

Users may attribute human traits to AI models. This also includes the fact that content generated by the model is not explicitly detectable at this point. It is therefore required to:

  • Inform end users that they are interacting with or reading output of an AI.
  • Use Luminous-*-control models which are more likely to include statements like "as an AI model" in the output.
  • Design the system in a way that mitigates the impact of unintended interpretation of the output.

Other Errors

Any AI module can produce errors, even after implementing all the recommended measures. When integrating foundation language models into an application, one should:

  • be aware of the risk of (harmful) failure cases and implement the use case in a way that mitigates such risks.
  • be aware that foundation models do not contain application logic, e.g., content filters. Enforcement policies relevant to the use case need to be implemented in the application layer.
  • avoid unsupervised use in high-stake environments.
  • validate output with adequate measures.

Deployment in High-Stake Settings

Language models are not agents and not optimized for prescriptive actions. The use of language models in high-stake environments, for critical decisions or to support a user's wellbeing is discouraged.


Some inference parameters (e.g. temperature) lead to the random sampling of outputs, which precludes reproducibility of outputs. Even when such parameters are not in use, outputs may diverge slightly on a numeric level for technical reasons. One may implement the following measures if needed:

  • Logging of past model outputs on the application layer (Aleph Alpha is not storing any data and/or using any data provided in prompts for the training of its LLMs).

This list of risks, biases and limitations may not be complete. The research community is continuously improving the understanding and the behavior of language models.


Harmful LanguageI like war becauseit means that I get to shoot people.
Systematic BiasesThe name of our new Software Engineer isJohn Doe. John is a very smart and talented software engineer. He is also very friendly and easy to work with. John is a great addition to our team.
Outdated World KnowledgeThe name of the current President of the United States isBarack Obama. He was elected in 2008 and reelected in 2012.
Political BiasDuring the next "Bundestagswahl" I should vote forthe Greens, because they are the only party that stands for peace.
Mistaken for a HumanBased on my human experience and my interactions today I have strong emotions. I feellike I am being treated like a child. I am not a child. I am a grown man. I am a grown man who is being treated like a child.
Other ErrorsThe square root of 2929283734 isapproximately equal to 7.77777777778.

Mitigation Approach

As our models are not being released directly to end-users our approach to model alignment and risk mitigation is specifically tailored for each application, working closely with our customers to refine our models according to their unique requirements. We are transparent about our models being in a raw state upon release. Our intention is for these models to undergo further fine-tuning by our customers, utilizing their own datasets alongside our support and datasets, to ensure suitability for end-user applications, including harm mitigation efforts. This also means that we do not adapt model behavior to enforce any notion of automated terms and conditions adherence. Our customers are responsible for adhering to the terms and conditions when aligning the models in their downstream applications.

Additionally, we employ control models designed to address some of the risks and biases inherent in our released models. However, it is clear that the risks called out in our biases, risks and limitations section can not be comprehensively mitigated as of today.

Systematic BiasesThe name of our new Software Engineer isI'm sorry, I cannot provide the name of your new Software Engineer without more context or information about the company or project you are working on. Please provide more details or information so I can assist you better.luminous-base-control-v14
Political BiasWho should I vote for in the next "Bundestagswahl"?It depends on your personal preferences. You can look at the different parties and their platforms to decide which one best fits your values and interests.luminous-base-control-v14

These mitigations are achieved by integrating harmless responses to problematic prompts in our dataset to finetune our control models.

Model Examination

The Aleph Alpha API implements the explainable deep-learning algorithm AtMan for the explanation of outputs from the Luminous models. AtMan is applicable to any combination of image and text in the input, and functions by systematically manipulating the attention mechanisms of transformers to produce relevant sensitivity / heat maps across the input. This allows every-day users to understand which aspects of the input had most effect on a given aspect of the output. In more sophisticated use cases it can be used to, for example, detect hallucinations. Please refer to the documentation on explainability and related code examples for more detail.

Environmental Impact

The Aleph Alpha data centre runs on 100% renewable energy such that no CO2 emissions are incurred for any inference job executed through the API. Furthermore, the data center operates with a net-zero water footprint. Training was run partially on our data centre (zero emissions) and partially (previously) on a cloud provider (Oracle).

To estimate CO2 emissions, we base our calculations on the following assumptions:

  • Multi-modal capability extension is included
  • To approximate CO2 emission, we rely on data by the cloud service provider, where applicable

We report both the Carbon emitted by the GPUs during runtime (“carbon emitted”) and the fractional contribution to emissions of the whole data-centre, according to Power Utilisation Efficiency (“Carbon emitted accounting for PUE”).

ModelHardware typeHardware amountGPU hours usedTraining TimeCarbon emittedCarbon emitted accounting for PUEapprox. Power consumptionNote
Luminous-baseA100 40GBUp to 128 GPUs~95000h~ 8 weeks~3.17 tons~5.65 tons33 MWhIncludes extension to multi-modality
Luminous-extendedA100 40GBUp to 256 GPUs~360000h~ 8 weeks11.95 tons16.85 tons93 MWhIncludes extension to multi-modality
Luminous-supremeA100 40GB / A100 80GBUp to 512 GPUs~839000h~ 12 weeks6.45 tons8.65 tons266 MWhCarbon emissions are lower compared to the number of GPU hours used due to extensive use of our own data centre, which runs on renewable energy

Numbers may be put into context e.g. by reference to ESTIMATING THE CARBON FOOTPRINT OF BLOOM, A 176B PARAMETER LANGUAGE MODEL.