Llama3 8b Cpt Sea Lionv2.1 Instruct
Llama3 8B CPT SEA-Lionv2.1 Instruct is a multilingual model that can understand and respond to instructions in multiple languages, including English, Indonesian, Thai, Vietnamese, and Tamil. But how does it work? This model was fine-tuned using a large dataset of instruction-completion pairs, with a focus on ensuring high-quality and commercially permissive data. The result is a model that can accurately follow instructions and engage in chat interactions. But what makes it unique? For one, it's designed to be efficient and fast, with a context length of 8192. It's also been evaluated on various tasks, including question answering, sentiment analysis, and translation. So, what can you use it for? From generating text to answering questions, this model is a versatile tool for anyone looking to work with multiple languages. Just keep in mind that, like many LLMs, it may occasionally generate irrelevant content or introduce fictional elements. But with its capabilities and efficiency, it's definitely worth exploring.
Table of Contents
Model Overview
The Llama3 8B CPT SEA-Lionv2.1 Instruct model is a multilingual language model designed for the Southeast Asia (SEA) region. It’s part of the SEA-LION collection of Large Language Models (LLMs) and has been fine-tuned with around 100,000 English instruction-completion pairs and 50,000 pairs from other ASEAN languages, such as Indonesian, Thai, and Vietnamese.
Key Features
- Multilingual: Supports English, Indonesian, Thai, Vietnamese, and Tamil languages
- Instruction-following: Capable of following instructions accurately
- High-quality datasets: Trained on carefully curated and rewritten datasets
- Commercially permissive: Released under the Llama3 Community License
Capabilities
This powerful multilingual model can understand and respond to instructions in multiple languages. But what can it do?
What can it do?
- Answer questions: It can process and respond to questions on a wide range of topics, from simple queries to more complex ones.
- Analyze sentiment: It can analyze text to determine the sentiment behind it, whether it’s positive, negative, or neutral.
- Detect toxicity: It can detect toxic language and alert you to potential issues.
- Translate text: It can translate text from one language to another, including English to Indonesian, Thai, Vietnamese, and more.
- Summarize content: It can summarize long pieces of text into shorter, more digestible versions.
- Reason and infer: It can use logic and reasoning to infer answers to questions and make decisions.
How does it work?
- Instruction-following: It has been fine-tuned to follow instructions accurately, making it a reliable tool for tasks that require specific guidance.
- Multilingual capabilities: It has been trained on a diverse range of languages, allowing it to understand and respond to instructions in multiple languages.
- High-quality datasets: It has been trained on high-quality datasets that have been carefully curated and verified to ensure accuracy and reliability.
What makes it unique?
- Southeast Asian languages: It is one of the few models that has been specifically designed to understand and respond to instructions in Southeast Asian languages.
- Commercially permissive licenses: It has been trained on datasets with commercially permissive licenses, making it a reliable choice for businesses and organizations.
Performance
But how well does it perform?
Speed
It has a context length of 8192, which is quite impressive. But what does this mean for its speed? In general, a longer context length allows the model to process more information at once, making it faster for certain tasks. However, it also increases the computational resources required.
Accuracy
Let’s take a look at the model’s performance on various tasks:
| Task | Accuracy |
|---|---|
| Question Answering (QA) | High |
| Sentiment Analysis (Sentiment) | High |
| Toxicity Detection (Toxicity) | High |
| Translation (Eng>Lang & Lang>Eng) | High |
| Abstractive Summarization (Summ) | Medium |
| Causal Reasoning (Causal) | Medium |
| Natural Language Inference (NLI) | Medium |
As you can see, it performs exceptionally well on tasks like Question Answering, Sentiment Analysis, and Toxicity Detection. However, its performance on tasks like Abstractive Summarization, Causal Reasoning, and Natural Language Inference is only medium.
Limitations
Like any model, it’s not perfect. What are some of its limitations?
Hallucinations and Irrelevant Content
It can sometimes generate responses that are not grounded in reality. This means it might introduce fictional elements or provide answers that are not based on actual facts. Be cautious when interpreting its responses, and always verify the information it provides.
Inconsistent Reasoning
Its reasoning can be inconsistent at times. This might lead to responses that don’t quite add up or seem contradictory. Keep this in mind when using the model, and don’t hesitate to ask for clarification if you’re unsure.
Safety Concerns
It has not been aligned for safety. This means it may not always provide responses that are respectful, fair, or free from bias. Developers and users should take extra precautions to fine-tune the model for safety and security.
Format
It uses a decoder architecture and accepts input in the form of text sequences. It’s capable of understanding and responding to instructions in multiple languages, including English, Indonesian, Thai, Vietnamese, and Tamil.
Supported Data Formats
The model supports text data in the form of instruction-completion pairs. It has been fine-tuned on a wide range of instructions that were manually verified and corrected by native speakers.
Input Requirements
To use the model, you need to provide input in the form of text sequences. The input should be a list of dictionaries, where each dictionary contains the role and content of the input. For example:
messages = [
{"role": "user", "content": "Apa sentimen dari kalimat berikut ini?\nKalimat: Buku ini sangat membosankan.\nJawaban: "},
]
Output Requirements
The model generates output in the form of text sequences. The output is a list of dictionaries, where each dictionary contains the generated text. For example:
outputs = pipeline(messages, max_new_tokens=256)
print(outputs[0]["generated_text"][-1])
