---
license: mit
base_model: meta-llama/Llama-3.2-3B
tags:
- llama-3.2
- unsloth
- fine-tuned
- gguf
- doctor
- dental
- medical
- chat
- instruction-tuning
datasets:
- BirdieByte1024/doctor-dental-llama-qa
---

# 🦷 doctor-dental-implant-llama3.2-3B-full-model

This model is a fine-tuned version of `meta-llama/Llama-3.2-3B`, trained using the [Unsloth](https://github.com/unslothai/unsloth) framework on a domain-specific instruction dataset focused on **medical** and **dental implant conversations**.

The model has been optimized for **chat-style reasoning** in doctor–patient scenarios, particularly within the domain of **Straumann® dental implant systems**, as well as general medical question answering.

---

## 🔍 Model Details

- **Base model:** `meta-llama/Llama-3.2-3B`
- **Training framework:** [Unsloth](https://github.com/unslothai/unsloth) with LoRA + QLoRA support
- **Training format:** Conversational JSON with `{"from": "patient"/"doctor", "value": ...}` messages
- **Checkpoint format:** Full model merged, usable as standard HF or GGUF (Ollama / llama.cpp)
- **Tokenizer:** Inherited from base model
- **Model size:** 3B parameters (efficient for consumer-grade inference)

---

## 📚 Dataset

This model was trained on:
- [`BirdieByte1024/doctor-dental-llama-qa`](https://huggingface.co/datasets/BirdieByte1024/doctor-dental-llama-qa)

The dataset contains synthetic and handbook-derived doctor-patient conversations focused on:
- Dental implant systems (e.g. surgical kits, guided procedures)
- General medical Q&A relevant to clinics and telemedicine
- Clinical assistant-style instruction-following

---

## 💬 Prompt Format

The model expects a **chat-style format**:

```
{
  "conversation": [
    { "from": "patient", "value": "What are the advantages of guided implant surgery?" },
    { "from": "doctor", "value": "Guided surgery improves accuracy, safety, and esthetic outcomes." }
  ]
}
```

---

## ✅ Intended Use

- Virtual assistants in dental or medical Q&A
- Instruction-tuned experimentation on health topics
- Local chatbot agents (Ollama / llama.cpp compatible)

---

## ⚠️ Limitations

- Model is not a medical device or diagnostic tool
- Hallucinations and factual errors may occur
- Content was fine-tuned using synthetic and handbook-based sources (not real EMR)

---

## 🧪 Example Prompt

```json
{
  "conversation": [
    { "from": "human", "value": "What should I expect after a Straumann implant surgery?" },
    { "from": "assistant", "value": "[MODEL RESPONSE HERE]" }
  ]
}
```

---

## 🛠 Deployment

### Local Use with Hugging Face Transformers

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BirdieByte1024/doctor-dental-implant-llama3.2-3B-full-model")
model = AutoModelForCausalLM.from_pretrained("BirdieByte1024/doctor-dental-implant-llama3.2-3B-full-model")
```

### GGUF / Ollama / llama.cpp

```bash
ollama run doctor-dental-llama3.2
```

> If using a local `Modelfile`, ensure the prompt template matches chat formatting (no Alpaca-style).

---

## ✍️ Author

Created by [(BirdieByte1024)](https://huggingface.co/BirdieByte1024) as part of a medical AI research project using Unsloth and LLaMA 3.2.

---

## 📜 License

MIT