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  LoRA adapter for Finnish dental medicine consultations between healthcare professionals. Trained on 30,908 synthetic dental conversations (80% expert clinical cases, 20% concept explanations) covering a broad range of different scenarios. Generated using pipeline with GPT-4.1.
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  **Research Focus:** This model demonstrates domain-specific fine-tuning with low computational resources. The goal is to explore how specialized models can be trained and deployed on consumer hardware (including personal GPUs) rather than requiring high-end infrastructure.
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  **System prompt recommendation:** This model was trained with a specific system prompt. For best results, we recommend using the same prompt format shown in the examples below.
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  ## Model Comparison
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- **LoRA Version**: Separate adapter files, requires base model
 
 
 
 
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- **Merged Version**: LoRA adapters merged into standalone model, no additional files needed
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  ## Installation
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  - Potential underrepresentation of certain patient demographics or clinical scenarios
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  ## Related Models
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- - Merged version: [ducklingcodehouse/Finnish-DentalQA-merged](https://huggingface.co/ducklingcodehouse/Finnish-DentalQA-merged)
 
 
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  - Base model: [Finnish-NLP/Ahma-3B-Instruct](https://huggingface.co/Finnish-NLP/Ahma-3B-Instruct)
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  ## Citation
 
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  LoRA adapter for Finnish dental medicine consultations between healthcare professionals. Trained on 30,908 synthetic dental conversations (80% expert clinical cases, 20% concept explanations) covering a broad range of different scenarios. Generated using pipeline with GPT-4.1.
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+ **Note:** Version 2 models are now available with enhanced training datasets (50,132 conversations) and improved clinical coverage including psychological, social, and ethical aspects. See [Finnish-DentalQA-v2-lora](https://huggingface.co/ducklingcodehouse/Finnish-DentalQA-v2-lora) and [Finnish-DentalQA-v2-merged](https://huggingface.co/ducklingcodehouse/Finnish-DentalQA-v2-merged).
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  **Research Focus:** This model demonstrates domain-specific fine-tuning with low computational resources. The goal is to explore how specialized models can be trained and deployed on consumer hardware (including personal GPUs) rather than requiring high-end infrastructure.
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  **System prompt recommendation:** This model was trained with a specific system prompt. For best results, we recommend using the same prompt format shown in the examples below.
 
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  ## Model Comparison
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+ **v1 LoRA Version**: Separate adapter files, requires base model
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+ **v1 Merged Version**: [ducklingcodehouse/Finnish-DentalQA-merged](https://huggingface.co/ducklingcodehouse/Finnish-DentalQA-merged) - LoRA adapters merged into standalone model, no additional files needed
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+ **v2 LoRA Version**: [ducklingcodehouse/Finnish-DentalQA-v2-lora](https://huggingface.co/ducklingcodehouse/Finnish-DentalQA-v2-lora) - Enhanced training (50,132 samples vs 30,908), improved clinical coverage, separate adapter files, requires base model
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+ **v2 Merged Version**: [ducklingcodehouse/Finnish-DentalQA-v2-merged](https://huggingface.co/ducklingcodehouse/Finnish-DentalQA-v2-merged) - Enhanced training (50,132 samples vs 30,908), improved clinical coverage, LoRA adapters merged into standalone model
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  ## Installation
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  - Potential underrepresentation of certain patient demographics or clinical scenarios
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  ## Related Models
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+ - v1 Merged version: [ducklingcodehouse/Finnish-DentalQA-merged](https://huggingface.co/ducklingcodehouse/Finnish-DentalQA-merged)
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+ - v2 LoRA version: [ducklingcodehouse/Finnish-DentalQA-v2-lora](https://huggingface.co/ducklingcodehouse/Finnish-DentalQA-v2-lora)
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+ - v2 Merged version: [ducklingcodehouse/Finnish-DentalQA-v2-merged](https://huggingface.co/ducklingcodehouse/Finnish-DentalQA-v2-merged)
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  - Base model: [Finnish-NLP/Ahma-3B-Instruct](https://huggingface.co/Finnish-NLP/Ahma-3B-Instruct)
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  ## Citation