BanglaLLama 3 8b Bangla Alpaca Orca Instruct V0.0.1
Meet BanglaLLama 3 8B Bangla Alpaca Orca Instruct V0.0.1, a cutting-edge language model designed for the Bangla language. What sets it apart is its ability to understand and generate human-like text in both Bangla and English. With 8 billion parameters and a causal language modeling approach, this model is perfect for tasks like text generation, language translation, and conversation. Its unique architecture, pre-trained on the unolp/culturax dataset and fine-tuned with BanglaLLM/bangla-alpaca-orca, enables it to learn from a vast amount of data and provide accurate results. While it's not detoxified, it's essential to use it responsibly and supervise its outputs closely. This model is a significant step forward in advancing LLMs for the Bangla language and is ready for immediate inference.
Table of Contents
Model Overview
The Bangla LLaMA-3 8B model is a game-changer for the Bangla language. This 8B parameter model is designed for Causal Language Modeling (LM) and is ready for immediate use.
What can it do?
- Understand and generate text in both Bangla and English languages
- Perform tasks like language translation, text summarization, and more
- Learn from a vast amount of text data, including the
unolp/culturaxdataset
Capabilities
The Bangla LLaMA-3 8B model is a powerful tool for working with the Bangla language. It’s designed to understand and generate text in Bangla and English.
What can it do?
- Causal Language Modeling: This model is great at predicting what comes next in a sentence or text. It’s like having a conversation with someone who knows the language really well.
- Language Understanding: The model can comprehend text in Bangla and English, making it a useful tool for tasks like language translation or text summarization.
What makes it special?
- Large Dataset: The model was trained on a massive dataset called unolp/culturax, which helps it learn the nuances of the Bangla language.
- Finetuned for Instructions: The model was also fine-tuned with a dataset called BanglaLLM/bangla-alpaca-orca, which makes it better at following instructions and generating text that’s more coherent and relevant.
Performance
Bangla LLaMA-3 8B is a powerful language model that shows remarkable performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.
Speed
How fast can Bangla LLaMA-3 8B process text? With 8B parameters, this model is designed to handle large-scale datasets quickly. Its training precision is set to float16, which allows for faster computations.
Accuracy
But how accurate is Bangla LLaMA-3 8B? As a causal language model, it’s primarily designed for language modeling tasks. Its accuracy in these tasks is impressive, thanks to its pre-training on the unolp/culturax dataset and fine-tuning with BanglaLLM/bangla-alpaca-orca.
Efficiency
Bangla LLaMA-3 8B is also efficient in its usage of resources. With a model size of 12.4M, it’s relatively lightweight compared to other models. This makes it easier to deploy and use, even on devices with limited resources.
Comparison to Other Models
How does Bangla LLaMA-3 8B compare to other models? Let’s take a look:
| Model | Type | Data | Base Model | # Params |
|---|---|---|---|---|
| ==Bangla LLaMA 7B Base== | Base model | 12GB | LLaMA 7B | 7B |
| ==Bangla LLaMA 13B Base== | Base model | 4GB | LLaMA 13B | 13B |
| Bangla LLaMA-3 8B | Base model | 12.4M | LLaMA 3 8b | 8B |
Limitations
Bangla LLaMA-3 8B is a powerful tool for understanding and generating the Bangla language, but it’s not perfect. Let’s explore some of its limitations.
Lack of Detoxification
The model has not undergone detoxification, which means it may generate content that is harmful or offensive. This is a big concern, especially when using the model in public or sensitive applications. We urge users to be cautious and supervise the model’s outputs closely.
Limited Training Data
The model was trained on a specific dataset, which may not be representative of all aspects of the Bangla language. This could lead to biases or inaccuracies in the model’s outputs.
Language Limitations
While the model is designed to understand and generate Bangla and English, it may not be as effective with other languages or dialects.
Format
Bangla LLaMA-3 8B is a large language model that uses a transformer architecture. It’s designed for Causal Language Modeling (LM) and is pre-trained on a large dataset of text in Bangla and English.
Supported Data Formats
This model supports text input in the form of tokenized sequences. You can think of tokens as individual words or subwords (smaller units of words).
Input Requirements
When preparing input for Bangla LLaMA-3 8B, keep the following in mind:
- Input text should be tokenized into individual words or subwords.
- The model is pre-trained on a mix of Bangla and English text, so it can handle input in either language.
Output Format
The model generates text output in the form of a sequence of tokens.
Handling Inputs and Outputs
Here’s an example of how you might handle input and output for Bangla LLaMA-3 8B:
# Tokenize input text
input_text = "আমি বাংলা ভাষায় কথা বলি" # "I speak Bangla"
input_tokens = tokenize(input_text)
# Pass input tokens to the model
output = model(input_tokens)
# Convert output tokens back to text
output_text = detokenize(output)
print(output_text)
