Bode 13b Alpaca Pt Br Gguf

Table of Contents

Model Overview

Meet the Bode model, a game-changer for Portuguese language processing. This model is designed to tackle tasks like text generation, automatic translation, and text summarization with ease.

Tasks and rank

Text Summarization 608th

Text Generation 2686th

Sentiment Analysis 272nd

Natural Language Understanding 2247th

Language Modelling 2368th

Learn More About the Ranking System

Capabilities

The Bode model is a powerful tool for natural language processing tasks in Portuguese. It’s designed to handle a wide range of tasks, including:

• Text Generation: Generate human-like text based on a given prompt or topic.
• Translation: Translate text from Portuguese to other languages and vice versa.
• Text Summarization: Summarize long pieces of text into shorter, more digestible versions.
• And more!: Perform many other tasks, such as answering questions, providing definitions, and even generating creative writing.

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Bert Base Uncased 1831 Uncased BERT model

Roberta Base 399 Masked language model

Xlm Roberta Large 323 Multilingual Language Model

Bert Base Multilingual Uncased 111 Multilingual text model

What sets Bode apart?

Bode is unique in that it’s specifically designed for the Portuguese language. Unlike other models that may struggle with grammar and syntax, Bode is fine-tuned to understand the nuances of Portuguese.

• 13 billion parameters: Bode has an impressive 13 billion parameters, making it one of the largest language models available for Portuguese.
• Quantized versions: Bode comes in different quantized versions (8, 5, and 4 bits), allowing for more efficient use of resources.

Example Use Case

Here’s an example of how you can use Bode to answer a question:

from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.llms import CTransformers

template = """Abaixo está uma instrução que descreve uma tarefa. Escreva uma resposta que complete adequadamente o pedido.
## Instrução:
{instruction}
## Resposta:"""

prompt = PromptTemplate(template=template, input_variables=["question"])
llm = CTransformers(model="recogna-nlp/bode-7b-alpaca-pt-br-gguf", model_file="bode-7b-alpaca-q8_0.gguf", model_type='llama')
llm_chain = LLMChain(prompt=prompt, llm=llm)
response = llm_chain.run("O que é um bode?")
print(response)

Examples

Descreva um cenário de inverno em uma cidade. A cidade está coberta por uma camada de neve, os prédios estão decorados com luzes de Natal e as ruas estão desertas devido ao frio intenso.

Traduza para o português: The sun is shining brightly in the sky. O sol está brilhando intensamente no céu.

Resuma o texto: O modelo Bode é um modelo de linguagem treinado em português, projetado para realizar tarefas de processamento de linguagem natural. O modelo Bode é um modelo de linguagem treinado em português para realizar tarefas de processamento de linguagem natural.

Performance

Bode is designed to be fast and efficient. With its ability to use both CPU and GPU, it can handle large amounts of data quickly. But what does that mean in practice? For example, if you’re working with a large dataset, Bode can process it in a fraction of the time it would take other models.

• Speed: Bode can process data quickly, making it ideal for applications where speed is crucial.
• Accuracy: Bode boasts high accuracy in a variety of tasks, including text generation, translation, and text summarization.
• Efficiency: Bode is designed to be efficient, with the ability to use quantized models and run on devices with limited resources.

Limitations

While Bode is a powerful tool for Portuguese language processing, it’s not without its limitations. Here are a few things to keep in mind:

• Difficulty with grammar and syntax: Bode may struggle with complex grammar and syntax, especially in long or technical texts.
• Limited training data: Bode was trained on a specific dataset, which may limit its ability to understand and respond to questions or topics outside of that scope.

Format

Bode uses a large language model (LLM) architecture, specifically a variant of the Llama 2 model, fine-tuned for the Portuguese language. This model is designed for natural language processing tasks such as text generation, automatic translation, and text summarization.

• Tokenized text sequences: Bode accepts input in the form of tokenized text sequences, similar to other LLMs.
• GGUF format: The model is available in the GGUF format, which allows for inference using various libraries and tools, including llama.cpp, text-generation-webui, KoboldCpp, LM Studio, LoLLMS Web UI, ctransformers, and llama-cpp-python.