achmadzanuar/legal-chatbot-indonesia
The achmadzanuar/legal-chatbot-indonesia is a 1.5 billion parameter Qwen2.5-based instruction-tuned causal language model developed by achmadzanuar. Finetuned using Unsloth and Huggingface's TRL library, it offers a 32768 token context length. This model is specifically optimized for legal chatbot applications in Indonesia, leveraging its efficient training for specialized domain performance.
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Model Overview
The achmadzanuar/legal-chatbot-indonesia is a specialized 1.5 billion parameter language model, finetuned from the unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit base model. Developed by achmadzanuar, this model leverages the Qwen2.5 architecture and was trained with enhanced efficiency using Unsloth and Huggingface's TRL library, resulting in a 2x faster training process.
Key Capabilities
- Domain-Specific Focus: Optimized for legal chatbot applications within the Indonesian context.
- Efficient Training: Benefits from Unsloth's optimizations for faster finetuning.
- Context Length: Supports a substantial context window of 32768 tokens, suitable for processing lengthy legal documents or conversations.
Good For
- Developing AI-powered legal assistants or chatbots for Indonesian users.
- Applications requiring understanding and generation of legal-specific text in Indonesian.
- Researchers and developers looking for a specialized, efficiently trained model for legal NLP tasks.