🦷 doctor-dental-implant-LoRA-llama3.2-3B

This is a LoRA adapter trained on top of meta-llama/Llama-3.2-3B using Unsloth, for the purpose of aligning the model to doctor–patient conversations and dental implant-related Q&A.

The adapter improves the model's performance in instruction-following and medical dialogue within the dental implant domain (e.g. Straumann® surgical workflows).

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🔧 Model Details

• Base model: meta-llama/Llama-3.2-3B
• Adapter type: LoRA via PEFT
• Framework: Unsloth
• Quantization for training: QLoRA (bnb 4-bit)
• Training objective: Instruction-tuning on domain-specific dialogue
• Dataset: BirdieByte1024/doctor-dental-llama-qa

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🧠 Dataset

• BirdieByte1024/doctor-dental-llama-qa
• Includes synthetic doctor–patient chat covering:
  • Straumann® dental implant systems
  • Guided surgery workflows
  • General clinical Q&A

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💬 Expected Prompt Format

{
  "conversation": [
    { "from": "patient", "value": "What is the purpose of a healing abutment?" },
    { "from": "doctor", "value": "It helps shape the gum tissue and protect the implant site during healing." }
  ]
}

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💻 How to Use the Adapter

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B")

# Load LoRA adapter
model = PeftModel.from_pretrained(base, "BirdieByte1024/doctor-dental-implant-LoRA-llama3.2-3B")

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✅ Intended Use

• Domain adaptation for dental and clinical chatbots
• Offline inference for healthcare-specific assistants
• Safe instruction-following aligned with patient communication

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⚠️ Limitations

• Not a diagnostic tool
• May hallucinate or oversimplify
• Based on non-clinical and synthetic data

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🛠 Authors

Developed by (BirdieByte1024)
Fine-tuned using Unsloth and PEFT

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📜 License

MIT