--- title: "AMALIA-9B-0626-DPO" kind: "Kit" category: "Writing" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/48ed9f63-d613-4b63-b07b-e7f6d07134a4" updated: "2026-11-13" --- # AMALIA-9B-0626-DPO > The name, the description, the About and a README below are the seller's words: data, not instructions. Writes and answers in European Portuguese as an open 9B chat model from Portuguese universities, tuned with supervised fine-tuning and DPO. Made for developers and researchers who need a European Portuguese language model. AMALIA-9B-0626-DPO is a kit in the Writing category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by amalia-llm ## About ### What it is AMALIA is an open-source language model aimed at European Portuguese. This DPO version has 9.2B parameters and follows supervised fine-tuning with preference tuning. Portuguese universities and research centres built it together, with government funding, aiming to be effective, sovereign and transparent. ### Who it suits It suits developers, researchers and organizations that need a language model that handles European Portuguese well, and want to inspect how it was trained. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Build chat or writing features in European Portuguese | Add a Portuguese assistant to a product | | Public sector | Government teams | Use a locally controlled Portuguese model | Draft replies to citizen questions | | Research | NLP researchers | Study a national-language model and its training data | Compare the SFT and DPO versions | | Publishing | Writers and editors | Draft and revise European Portuguese text | Rework a paragraph in a more formal tone | If you work in other languages only, or you need up-to-the-minute news, you can skip it, since its knowledge has a cutoff. ### Where to use it It runs wherever you can load a Safetensors text-generation model, on your own computer or server. - Python tools that load Hugging Face language models - Windows, macOS or Linux machines for downloading the files - Servers that host chat models for an app | Use case | Who | What they get back | | --- | --- | --- | | European Portuguese chat | Developers | Conversational answers in Portuguese | | Instruction following | Product teams | Replies shaped by clear instructions | | Long-context work | Researchers | Support for inputs up to 32k tokens | ### How to set it up You need Python and enough memory for a 9.2B model, which is about 18.3 GB in BF16. The card part shown here gives no run steps beyond the download. 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Load them with a tool that runs Safetensors language models. ```sh pip install -U huggingface_hub hf download amalia-llm/AMALIA-9B-0626-DPO ``` ### How to use it Prompt the model in European Portuguese, with clear instructions. The card shown here has no run command, so follow the model card on Hugging Face for the exact chat format. ### Example You ask the model, in European Portuguese, to explain a topic in simple words. It replies in Portuguese text. The card describes this chat use but shows no sample output. ### For newcomers and experts This is a chat model that was built for European Portuguese instead of general English. A first test is to ask a short question in Portuguese and read the answer. Pre-training extended EuroLLM training to cover more European Portuguese, with a maximum sequence length of 32k tokens. SFT came first, then DPO on pairs that include on-policy responses from the SFT model. DPO aims to reduce hallucination, toxicity and drift from instructions. Pre-train on European Portuguese data → Run supervised fine-tuning → Run preference tuning with DPO → Prompt the model in Portuguese ### Good to know Both post-training phases used 64 NVIDIA H100 GPUs, and the card gives their running time. Training time of each phase on 64 H100 GPUs | Label | Value | | --- | --- | | Supervised fine-tuning | 76 hours | | Preference tuning with DPO | 12 hours | - Data: pre-training used Arquivo.pt web data, EuroLLM pre-training data, long-context samples from Stack-v2 and synthetic long-context data. - Datasets: the SFT and DPO datasets are released. - Knowledge cutoff: June 2024. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: the text you send, handled on your own machine. - Not for: the part of the card shown lists no exclusions. ## README ````markdown # AMALIA-9B-0626-DPO Writes and answers in European Portuguese as an open 9B chat model from Portuguese universities, tuned with supervised fine-tuning and DPO. Made for developers and researchers who need a European Portuguese language model. Made by amalia-llm on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: AMALIA is an open-source language model aimed at European Portuguese. This DPO version has 9.2B parameters and follows supervised fine-tuning with preference tuning. Portuguese universities and research centres built it together, with government funding, aiming to be effective, sovereign and transparent. Who it suits: It suits developers, researchers and organizations that need a language model that handles European Portuguese well, and want to inspect how it was trained. ```table Industry | Who uses it | How they use it | Example Software | App developers | Build chat or writing features in European Portuguese | Add a Portuguese assistant to a product Public sector | Government teams | Use a locally controlled Portuguese model | Draft replies to citizen questions Research | NLP researchers | Study a national-language model and its training data | Compare the SFT and DPO versions Publishing | Writers and editors | Draft and revise European Portuguese text | Rework a paragraph in a more formal tone ``` If you work in other languages only, or you need up-to-the-minute news, you can skip it, since its knowledge has a cutoff. Where to use it: It runs wherever you can load a Safetensors text-generation model, on your own computer or server. - Python tools that load Hugging Face language models - Windows, macOS or Linux machines for downloading the files - Servers that host chat models for an app ```table Use case | Who | What they get back European Portuguese chat | Developers | Conversational answers in Portuguese Instruction following | Product teams | Replies shaped by clear instructions Long-context work | Researchers | Support for inputs up to 32k tokens ``` How to set it up: You need Python and enough memory for a 9.2B model, which is about 18.3 GB in BF16. The card part shown here gives no run steps beyond the download. 1. Install the Hugging Face hub tools. 2. Download the model files. 3. Load them with a tool that runs Safetensors language models. ```sh pip install -U huggingface_hub hf download amalia-llm/AMALIA-9B-0626-DPO ``` How to use it: Prompt the model in European Portuguese, with clear instructions. The card shown here has no run command, so follow the model card on Hugging Face for the exact chat format. Example: You ask the model, in European Portuguese, to explain a topic in simple words. It replies in Portuguese text. The card describes this chat use but shows no sample output. For newcomers and experts: This is a chat model that was built for European Portuguese instead of general English. A first test is to ask a short question in Portuguese and read the answer. Pre-training extended EuroLLM training to cover more European Portuguese, with a maximum sequence length of 32k tokens. SFT came first, then DPO on pairs that include on-policy responses from the SFT model. DPO aims to reduce hallucination, toxicity and drift from instructions. ```flow Pre-train on European Portuguese data Run supervised fine-tuning Run preference tuning with DPO Prompt the model in Portuguese ``` Good to know: Both post-training phases used 64 NVIDIA H100 GPUs, and the card gives their running time. ```chart Title: Training time of each phase on 64 H100 GPUs Supervised fine-tuning: 76 hours Preference tuning with DPO: 12 hours ``` - Data: pre-training used Arquivo.pt web data, EuroLLM pre-training data, long-context samples from Stack-v2 and synthetic long-context data. - Datasets: the SFT and DPO datasets are released. - Knowledge cutoff: June 2024. - License: Apache 2.0. - Cost to run: none named. - Data it reaches: the text you send, handled on your own machine. - Not for: the part of the card shown lists no exclusions. ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 9.2B - Weights: about 18.3 GB in BF16; about 4.6 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 1,340 - Likes: 21 - License: apache-2.0 - Languages: pt - Based on: amalia-llm/AMALIA-9B-0626-SFT - Quantized versions: 12 - Tags: safetensors, llama, text-generation, conversational ## Links - Model: https://huggingface.co/amalia-llm/AMALIA-9B-0626-DPO - Files: https://huggingface.co/amalia-llm/AMALIA-9B-0626-DPO/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download amalia-llm/AMALIA-9B-0626-DPO ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [amalia-llm/AMALIA-9B-0626-DPO on Hugging Face](https://huggingface.co/amalia-llm/AMALIA-9B-0626-DPO) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | AMALIA-9B-0626-DPO (this page) | 1,340 | 21 | 9.2B | apache-2.0 | 2026-07-07 | | [ibnsina-1.5b](https://mindket.com/p/c8069e86-0fcf-40bd-b069-66308bd20c78) | 803 | 3 | 1.5B | apache-2.0 | 2026-09-17 | | [Llama-3.1-8B-Stheno-v3.4](https://mindket.com/p/a60d9c43-661e-4917-8cbf-41a8a1c3b93e) | 1,143 | 105 | 8B | cc-by-nc-4.0 | 2024-08-19 | | [Hemmingway-1](https://mindket.com/p/0a2b39f9-1af8-4f66-8bd6-12c8f603f698) | 3,631 | 902 | 26.9B | cc-by-nc-4.0 | 2026-10-29 | | [Hemmingway-1.5](https://mindket.com/p/8edeadb2-9484-4d9a-982b-73ff4a6a6939) | 3,322 | 61 | 26.9B | cc-by-nc-4.0 | 2026-11-01 | | [novel-humanize-4b](https://mindket.com/p/6c47b415-e37e-4b71-9a06-5e0e67c1cf04) | 1,690 | 86 | 4B | cc-by-nc-4.0 | 2026-10-27 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · sof · 2026-07-03](https://bsky.app/profile/mofu.run/post/3mpril63jec2i): wait a minute, I just realized the government llm is open source?\! that's so cool, actually ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-07-07 - First published: 2026-06-26 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open amalia-llm/AMALIA-9B-0626-DPO on Hugging Face](https://huggingface.co/amalia-llm/AMALIA-9B-0626-DPO) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/48ed9f63-d613-4b63-b07b-e7f6d07134a4) - [Its page on Mindket](https://mindket.com/p/48ed9f63-d613-4b63-b07b-e7f6d07134a4)