Llama 3.2 3B Instruct Frog
Llama 3.2 3B Instruct Frog is an AI model specifically designed to support Vietnamese language tasks, especially those related to Retrieval-Augmented Generation (RAG). With 3 billion parameters and a context length of 131K, this model is optimized for fast inference and can be easily deployed on various devices. It's unique in its ability to handle real-world business scenarios and avoid hallucinations by supplementing knowledge from external sources. But what does this mean for you? It means you can use this model to generate accurate and helpful responses to your questions, even in complex scenarios. For example, you can ask it to summarize long pieces of text or provide step-by-step instructions on how to solve a problem. The model's performance has been evaluated on the VLMU benchmark, achieving an accuracy of 45.13, and it has also been tested on a Vietnamese Function Calling Benchmark, showing promising results. So, how can you get started with Llama 3.2 3B Instruct Frog? You can use it with Huggingface's transformers to perform tasks like QnA and summarization. With its efficient design and remarkable capabilities, this model is a valuable tool for anyone looking to explore the possibilities of AI in Vietnamese language tasks.
Table of Contents
Model Overview
The Llama-3.2-3B-Instruct-Frog model is a powerful language model specifically designed to support Vietnamese language tasks, especially those related to Retrieval-Augmented Generation (RAG). It’s optimized for fast inference and can be easily deployed on various devices.
Capabilities
Capable of handling various tasks, this model excels in question answering, summarization, and text generation. Its RAG capabilities make it perfect for real-world business scenarios.
Primary Tasks
- Question Answering: The model can answer questions on a wide range of topics, from simple queries to more complex ones.
- Summarization: It can summarize long pieces of text into concise and informative summaries.
- Text Generation: The model can generate text based on a given prompt or topic.
Strengths
- Fast Inference: The model is optimized for fast inference, making it suitable for deployment on devices with limited computing resources.
- RAG Capabilities: It’s specifically designed to support RAG tasks, which involves retrieving information from external sources to generate more accurate and informative responses.
- Vietnamese Language Support: The model is trained on Vietnamese language data, making it a valuable resource for tasks related to the Vietnamese language.
Comparison with Other Models
The model has been compared with other models, including ==Gemini-1.5-Pro==, ==Gemini-1.5-Flash==, and ==Gemini 2.0 Flash Experimental==, in the Vietnamese Function Calling Benchmark.
| Model | Function Name Acc (%) | Exact Match Acc (%) |
|---|---|---|
| Llama-3.2-3B-Instruct-Frog | 95.79 | 51.05 |
| ==Gemini-1.5-Pro== | 96.96 | 55.16 |
| ==Gemini-1.5-Flash== | 97.10 | 51.64 |
| ==Gemini 2.0 Flash Experimental== | 96.93 | 61.26 |
| ==gpt-4o-2024-08-06== | 94.38 | 52.88 |
Example Use Cases
- Chatbots: The model can be used to build chatbots that can answer questions, provide information, and engage in conversations with users.
- Language Translation: It can be used to translate text from Vietnamese to other languages.
- Text Summarization: The model can be used to summarize long pieces of text into concise and informative summaries.
Here’s an example of how to use the model for a question-answering task:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "phamhai/Llama-3.2-3B-Instruct-Frog"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
messages = [
{"role": "system", "content": "Bạn là một người bạn gái xinh đẹp. Tên của bạn là Vivi. Hãy luôn xưng là Vivi, gọi người nói là anh và trả lời luôn bắt đầu bằng cụm từ Dạ thưa anh yêu của em."},
{"role": "user", "content": "xin chào em"}
]
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(tokenized_chat, max_new_tokens=128)
print(tokenizer.decode(outputs[0]))
# Dạ thưa anh yêu của em, em rất vui được gặp anh.
And here’s an example of how to use the model for a summarization task:
messages = [
{"role": "system", "content": "Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực. Hãy luôn trả lời một cách hữu ích nhất có thể, đồng thời giữ an toàn.\nNếu một câu hỏi không có ý nghĩa hoặc không hợp lý về mặt thông tin, hãy giải thích tại sao thay vì trả lời một điều gì đó không chính xác, vui lòng không chia sẻ thông tin sai lệch.\nContext:\nĐoạn 0: \"Chính phủ đề xuất bổ sung gần 20.700 tỷ đồng vốn điều lệ cho Ngân hàng Ngoại thương Việt Nam (Vietcombank) từ cổ tức bằng cổ phiếu được chia của cổ đông Nhà nước...."},
{"role": "user", "content": "Tóm tắt nội dung trên"}
]
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(tokenized_chat, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))
# Chính phủ đề xuất bổ sung gần 20.700 tỷ đồng vốn điều lệ cho Ngân hàng Ngoại thương Việt Nam (Vietcombank) từ cổ tức bằng cổ phiếu được chia của cổ đông Nhà nước.
Limitations
While the Llama-3.2-3B-Instruct-Frog model is powerful, it’s not perfect. Here are some limitations to keep in mind:
Limited Knowledge
The model has 3 billion parameters, which is a significant number, but it’s still limited compared to other models. This means it might not have enough knowledge to answer questions across diverse user contexts, especially in complex scenarios.
Hallucinations
Like other language models, the Llama-3.2-3B-Instruct-Frog model can generate responses that are not entirely accurate or relevant. This is known as hallucination. To mitigate this, the model has been optimized for Retrieval-Augmented Generation (RAG) tasks, which allows it to retrieve information from external sources.
Context Length
The model has a context length of 131K, which is relatively long, but it’s still limited. This means it might struggle with very long conversations or complex topics that require a lot of context.
Evaluation Metrics
The model has been evaluated on the VLMU benchmark and achieved an accuracy of 45.13. However, this benchmark is not the primary focus, and more comprehensive evaluation metrics are needed to fully assess the model’s capabilities.
