This is the model card for Gervásio 8B PTPT decoder quantized at 4 bits.
This model is integrated in the Evaristo.ai chatbot, where its generative capabilities can be experimented with on the fly through a GUI.
You may be interested in some of the other models in the Albertina (encoders), Gervásio (decoders) and Serafim (sentence encoder) families.

Gervásio 8B PTPT is an open decoder for the Portuguese language.

It is a decoder of the LLaMA family, based on the neural architecture Transformer and developed over the LLaMA 3.1 8B Instruct model. Its further improvement through additional training was done over language resources that include data sets of Portuguese prepared for this purpose, that include extraGLUE-Instruct , as well as other data sets whose release is being prepared (MMLU PT, Natural Instructions PT, Wikipedia subset, Provérbios PT).

Gervásio 8B PTPT is openly distributed for free under an open license, including thus for research and commercial purposes, and given its size, can be run on consumer-grade hardware.

Gervásio 8B PTPT is developed by NLX-Natural Language and Speech Group, at the University of Lisbon, Faculty of Sciences, Department of Informatics, Portugal.

For the record, its full name is Llama 3 é a base para Gervásio Produzir Textos em Português, to which corresponds the natural acronym GPT PT, and which is known more shortly as Gervásio PT* or, even more briefly, with the nickname Gervásio, among its acquaintances.

Gervásio 8B PTPT is developed by a team from the University of Lisbon, Portugal.

The model in this repository is a version of Gervásio 8B PTPT quantized at 4 bits (Q4_K_M). The non-quantized version can be found here.

The model has 8 billion parameters, over 32 layers, with a hidden size of 4096, an intermediate size of 14336, and 32 attention heads. It uses a RoPE tokenizer with a vocabulary of size 128256.

Gervásio 8B PTPT was trained on various datasets, either native to European Portuguese or translated into European Portuguese. For the latter, we selected only those datasets where the outcome of their translation into European Portuguese could preserve, in the target language, the linguistic properties at stake.

The training data comprises:

We applied supervised fine-tuning with a causal language modeling training objective following a zero-out technique during the fine-tuning process. Specifically, while the entire prompt and chat template received attention during fine-tuning, only the response tokens were subjected to back-propagation.

To accelerate training, the Fully Sharded Data Parallel (FSDP) paradigm was used over 10 L40S GPUs.

For testing, we use translations of the standard benchmarks GPQA Diamond, MMLU and MMLU Pro, as well as the CoPA, MRPC and RTE datasets in extraGLUE.

You can use this model directly with a pipeline for causal language modeling:

Please use the above canonical reference when using or citing this model.

The research reported here was partially supported by: PORTULAN CLARIN—Research Infrastructure for the Science and Technology of Language, funded by Lisboa 2020, Alentejo 2020 and FCT—Fundação para a Ciência e Tecnologia under the grant PINFRA/22117/2016; innovation project ACCELERAT.AI - Multilingual Intelligent Contact Centers, funded by IAPMEI, I.P. - Agência para a Competitividade e Inovação I.P. under the grant C625734525-00462629, of Plano de Recuperação e Resiliência, call RE-C05-i01.01 – Agendas/Alianças Mobilizadoras para a Reindustrialização; research project "Hey, Hal, curb your hallucination! / Enhancing AI chatbots with enhanced RAG solutions", funded by FCT-Fundação para a Ciência e a Tecnologia under the grant 2024.07592.IACDC; project "CLARIN – Infraestrutura de Investigação para a Ciência e Tecnologia da Linguagem", funded by programme Lisboa2030 under the grant LISBOA2030-FEDER-01316900PORTULAN.