Portugal releases Amália, open-source 9B parameter AI model trained on European Portuguese
Portugal has released Amália, its first national AI model trained specifically for European Portuguese. Built on EuroLLM-9B with 9 billion parameters, the model is fully open-source with weights, datasets, and code published under an open license. The government has committed €5.5m in initial funding through 2027.
Portugal releases Amália, open-source 9B parameter AI model trained on European Portuguese
Portugal has released Amália, its first national AI model trained specifically for European Portuguese, with the model's weights, training data, and source code all published under an open license. The 9 billion parameter model is built on EuroLLM-9B, a European foundation model, and represents a €5.5m initial investment through Portugal's Recovery and Resilience Plan.
Technical specifications
Amália—Automatic Multimodal Language Assistant with Artificial Intelligence—is based on the EuroLLM-9B foundation model. A team of more than 60 researchers and students expanded it with European Portuguese datasets, added a larger context window than the base model, strengthened safety and evaluation systems, and built in multimodal capabilities to handle both images and text.
The test version was completed in September 2025 and presented at the PROPOR conference in Brazil. Funding is secured through the end of 2027.
Deployment model: Infrastructure, not consumer product
Amália is not designed as a consumer-facing chatbot. Instead, it functions as foundational infrastructure that other software can call on. Planned applications include an AI teaching assistant, a virtual guide for Portuguese museums and monuments, a digital assistant for citizen services, and decision-support tools for the Portuguese Navy.
The model is distributed through NOVA University Lisbon, Instituto Superior Técnico, and the universities of Porto, Minho, and Coimbra, coordinated with the Foundation for Science and Technology.
European Portuguese vs Brazilian Portuguese
European Portuguese differs significantly from Brazilian Portuguese in grammar, idiom, and cultural references. Large commercial models, trained predominantly on Brazilian Portuguese, tend to flatten these distinctions. Amália addresses this gap for Portugal's 10 million citizens and public services that require accurate cultural and linguistic register.
The release follows the OpenEuroLLM alliance and broader European efforts to reduce dependence on American and Chinese AI systems for foundational language tasks. It coincides with infrastructure investments including Nscale's €695m data-centre expansion in Portugal with Microsoft.
Open license details
Unlike commercial models accessed through APIs and usage fees, Amália ships with its weights, datasets, and code published under an open license. Governments, universities, and companies can inspect training data, adapt the model, and run it on their own hardware without recurring costs or dependencies on external providers.
This approach enables the auditing and verification that Portugal's government considers necessary for systems integrated into citizen services and military applications.
What this means
Portugal's release of Amália represents a concrete implementation of digital sovereignty—not just stated policy but functioning infrastructure with multi-year funding commitments. The model's value lies in its specificity: accurate European Portuguese for a defined user base, not a scaled-down competitor to GPT-4 or Claude. Success depends on actual adoption by universities, companies, and government departments over the next two years. Publishing the model is the easy part; integration into production systems is where most national AI projects stall. Portugal has structured funding and institutional coordination through 2027, which provides a realistic timeline to determine whether Amália becomes operational infrastructure or remains a well-documented research artifact.
Related Articles
Qwen Launches Qwen3.8 27B, an Open-Weight Vision-Language Model with 262K Context
Qwen has released Qwen3.8 27B, a 27-billion-parameter dense vision-language model with a 262K token context window, available now via OpenRouter at $0.45 per million input tokens and $3.20 per million output tokens.
Alibaba Releases Qwen3.8-27B, a Dense Vision-Language Model with 1M-Token Context
Alibaba's Qwen team has released Qwen3.8-27B, a 27-billion-parameter dense vision-language model with 262,144-token native context extensible to 1 million tokens. The model shows gains over Qwen3.6-27B and Qwen3.7-Plus across coding, agentic, and multimodal benchmarks, according to Alibaba.
Liquid AI Releases LFM2.5-VL-3B, a 3B-Class Vision-Language Model Built for On-Device Deployment
Liquid AI has released LFM2.5-VL-3B, a multimodal upgrade to its LFM2-VL-3B model built for on-device grounding, object detection, and document OCR. The model runs at 228 tokens/sec on an Apple M5 Max and 116 tokens/sec on an AMD Ryzen AI Max+ 395, using under 3.3 GB of memory.
Z.ai Releases GLM-5.3, Claims Frontier Coding Scores From a 750B-Parameter Model
Z.ai released GLM-5.3, a coding-focused model built on the same base as GLM-5.2 but with substantially extended post-training, and claims it surpasses Moonshot AI's Kimi K3 on many agentic coding benchmarks despite having roughly a third of the parameters. The model is live in Z.ai's coding plan now, with API and open-weight Hugging Face access expected within two weeks.
Comments
Loading...