amalia-llm

AMALIA-VL-DPO

README

License: apache-2.0

Ethical Considerations and Risk

Developing large language models raises a number of ethical concerns. By releasing AMALIA as an open model for European Portuguese, the consortium considered the following:

Bias and fairness. Language models trained on large-scale, real-world text reflect the socio-cultural biases present in their training material. The model is also explicitly targeted at European Portuguese, and its handling of the pt-PT/pt-BR distinction is itself a fairness consideration that the consortium treats as a first-class evaluation dimension.
Misinformation and misuse. Like any LLM, AMALIA can produce text that is false, misleading, or harmful. Developers building on AMALIA are encouraged to communicate these limitations to end users and to provide mechanisms for reporting misuse.
Transparency and accountability. This card documents the model's training data, training process, and intended and out-of-scope uses so that developers and researchers can make informed decisions. Releasing AMALIA openly is intended to make European Portuguese language technology accessible to the wider research and developer community.

Risks identified and mitigations:

Generation of harmful content. AMALIA's post-training included safety data in both the SFT and DPO phases, and the DPO phase explicitly targets reductions in toxicity and harmful output. These measures reduce but do not eliminate the risk; downstream developers should add their own content-safety safeguards appropriate to their product and use case.
Misuse for malicious purposes. The consortium provides documentation of the model's intended use and limitations, and encourages developers who build on AMALIA to establish their own mechanisms for users to report misuse within their applications.
Perpetuation of biases. Continuous monitoring, through evaluation metrics and human review, and the use of de-biasing techniques during training, fine-tuning, and downstream adaptation were encouraged to limit the reinforcement of existing biases.
Adversarial inputs and prompt injection*.* Applications built on AMALIA may be vulnerable to prompt injection and other adversarial inputs. Developers should apply input validation and output filtering appropriate to their deployment context.

Citation

If you use AMALIA-VL in your work, please cite:

bibtex
@article{gloria2026amalia,
    title={AMALIA-VL: A Native European Portuguese Open-Source Vision and Language Model},
    author={Gl{\'o}ria-Silva, Diogo and Cardeira, Jo{\~a}o and da Luz, Manuel Letras and Simpl{\'\i}cio, Afonso and Vinagre, Gon{\c{c}}alo and Tavares, Diogo and Ferreira, Rafael and Calvo, In{\^e}s and Vieira, In{\^e}s and Semedo, David and others},
    journal={arXiv preprint arXiv:2606.19100},
    year={2026}
}

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Model Details

Model Provider

amalia-llm

Model Tree

Base

amalia-llm/AMALIA-VL-SFT

Fine-tuned

this model

Input Modalities

TextImage

Output Modalities

Text

Supported Functionality