tags:
- text-generation
- pytorch
inference: false
license: cc-by-4.0
language:
- pt
pipeline_tag: text-generation
library_name: transformers
Canarim-7B
Canarim-7B is a Portuguese language model developed by Maicon Domingues.
Model description
The model was pretrained on 16 billion tokens from the Portuguese subset of CommonCrawl 2023-23, starting with the weights of LLaMA2-7B. The pretraining data has cutoff of mid-2023.
Key Features
- Language: Specialized in understanding and generating Portuguese text, making it ideal for applications targeting Portuguese-speaking audiences.
- Architecture: Inherits the robust architecture from LLaMA2-7B, ensuring efficient performance and accurate results.
- Diverse Dataset: The pretraining dataset includes a wide range of topics and writing styles, enhancing the model's ability to understand various contexts and nuances in Portuguese.
Applications
Canarim-7B, was trained solely on a language modeling objective and has not been fine-tuned for instruction following. Therefore, it is more suited for few-shot tasks rather than zero-shot tasks. This means the model tends to perform better when provided with a few examples of the desired outcome during use. Here are some practical applications:
- Natural Language Understanding (NLU): Efficient in tasks such as sentiment analysis, topic classification, and entity recognition in Portuguese text, especially when relevant examples are provided.
- Natural Language Generation (NLG): Capable of generating coherent and contextually relevant text, useful for content creation, chatbots, and more, with improved results when provided examples of the desired style or format.
- Language Translation: Suitable for high-quality translation between Portuguese and other languages, especially when examples of desired translations are included during model training or fine-tuning.
Tips for Efficient Use
- Few-shot Learning: When using Canarim-7B for specific tasks, it is beneficial to provide a few relevant examples. This helps the model better understand the context and purpose of the task.
- Contextualization: Including additional context in the input can significantly improve the quality of the model’s predictions and text generation.
Getting Started
To start using Canarim-7B with the Transformers library, first install the library if you haven't already:
pip install transformers
You can then load the model using the Transformers library. Here's a simple example of how to use the model for text generation using the pipeline function:
from transformers import AutoTokenizer, pipeline
import torch
model_id = "dominguesm/canarim-7b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.float16,
device_map="auto",
)
prompt = make_prompt(question)
sequences = pipe(
prompt,
do_sample=True,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=2048,
temperature=0.9,
top_p=0.6,
repetition_penalty=1.15
)
This code snippet demonstrates how to generate text with Canarim-7B. You can customize the input text and adjust parameters like max_length according to your requirements.
License
Canarim-7B is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows others to copy, distribute, remix, adapt, and build upon the work, even commercially, as long as they credit the original creation.