sabia-7b
Maintainer: maritaca-ai
81
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
sabia-7b is a Portuguese language model developed by Maritaca AI. It is an auto-regressive language model that uses the same architecture as LLaMA-1-7B and the same tokenizer. The model was pretrained on 7 billion tokens from the Portuguese subset of ClueWeb22, starting with the weights of LLaMA-1-7B and further trained for an additional 10 billion tokens. Compared to similar models like Sensei-7B-V1, sabia-7b is tailored specifically for the Portuguese language.
Model inputs and outputs
sabia-7b is a text-to-text model, accepting only text input and generating text output. The model has a maximum sequence length of 2048 tokens.
Inputs
- Text: The model accepts natural language text as input.
Outputs
- Text: The model generates natural language text as output.
Capabilities
sabia-7b is capable of performing a variety of natural language processing tasks in Portuguese, such as text generation, translation, and language understanding. Due to its large training dataset and robust architecture, the model can generate high-quality, coherent Portuguese text across a range of topics and styles.
What can I use it for?
sabia-7b can be a valuable tool for developers and researchers working on Portuguese language applications, such as chatbots, content generation, and language understanding. The model can be fine-tuned or used in a few-shot manner for specific tasks, like the example provided in the model description.
Things to try
One interesting aspect of sabia-7b is its ability to effectively utilize the LLaMA-1-7B architecture and tokenizer, which were originally designed for English, and adapt them to the Portuguese language. This suggests the model may have strong cross-lingual transfer capabilities, potentially allowing it to be fine-tuned or used in a few-shot manner for tasks involving multiple languages.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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