robinsyihab/Sidrap-7B-v2
Sidrap-7B-v2 by robinsyihab is a 7 billion parameter causal language model, fine-tuned from Sidrap-7B-v1, specifically optimized for high-quality dialogue in Bahasa Indonesia. This model excels in generating helpful, respectful, and honest responses in Indonesian, making it one of the best open-source LLMs available for Bahasa Indonesia. It is designed for conversational AI applications requiring strong performance in the Indonesian language.
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Overview
Sidrap-7B-v2 is a 7 billion parameter large language model developed by robinsyihab, building upon its predecessor, Sidrap-7B-v1. This model is specifically fine-tuned using a carefully curated, high-quality Bahasa Indonesia dataset, positioning it as a leading open-source LLM for Indonesian dialogue.
Key Capabilities
- Optimized for Bahasa Indonesia: Designed to provide high-quality, contextually relevant responses in Indonesian.
- Dialogue Generation: Excels in conversational AI tasks, generating helpful and respectful dialogue.
- Instruction Following: Demonstrates strong ability to follow instructions, especially when provided with a system message for optimal performance.
Usage Considerations
- System Message Recommended: For best results, users should include a system message as the initial input, as demonstrated in the provided usage examples.
- Quantized Version Available: A 4-bit quantized version,
Sidrap-7B-v2-GPTQ-4bit, is available for more efficient deployment.
Limitations and Ethical Considerations
As a model trained primarily on public datasets, Sidrap-7B-v2 lacks an inherent moderation mechanism. Users are advised to exercise caution and review generated outputs for potential biases, harmful content, or other issues. It is crucial to align its usage with ethical guidelines, respect privacy, and avoid generating harmful content.