AITeamVN/Vi-Qwen2-3B-RAG
AITeamVN/Vi-Qwen2-3B-RAG is a 3.1 billion parameter language model, fine-tuned from the Qwen2-Instruct base model, specifically optimized for Retrieval Augmented Generation (RAG) tasks in Vietnamese. It excels at extracting useful information from noisy documents, rejecting answers when information is absent, integrating information from multiple documents, and accurately identifying positive/negative contexts. This model is designed to enhance Vietnamese language processing capabilities and improve RAG performance, supporting context lengths up to 8192 tokens.
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Overview
AITeamVN/Vi-Qwen2-3B-RAG is a 3.1 billion parameter large language model, fine-tuned from the Qwen2-Instruct base model, specifically designed for Retrieval Augmented Generation (RAG) tasks in Vietnamese. It aims to significantly improve Vietnamese language processing and RAG performance, supporting a context length of up to 8192 tokens. While specialized for RAG, it can also function as a general-purpose chatbot, handling continuous questions within an input context.
Key Capabilities
- Noise Robustness: Effectively extracts useful information from noisy documents, even with mixed positive and negative contexts.
- Negative Rejection: Accurately declines to answer questions when the necessary knowledge is not present in the retrieved documents.
- Information Integration: Answers complex questions by synthesizing information from multiple documents.
- Context Identification: Determines with approximately 99% accuracy whether a context contains the answer to a given question.
- Multipurpose: Can be used for standard chatbot interactions and continuous questioning within a provided context, beyond its primary RAG focus.
Limitations
Due to its specialized RAG design, the model may have limitations regarding:
- Accuracy on questions related to politics or social issues.
- Potential for exhibiting biases or inappropriate viewpoints.
Benchmarks
The model's RAG performance was evaluated using a manually created Vietnamese dataset, EvalRAGData, with human scoring. Additionally, it was benchmarked on the VMLU leaderboard, achieving an average score of 56.04 across various categories.