Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+  - pharmacy
+  - clinical-decision-support
+  - medication-review
+  - mistral
+  - fine-tuned
+base_model: mistralai/Mistral-7B-Instruct-v0.1
+model_name: markoste-clinical-mistral
+language: en
+datasets:
+  - Markolo96/Mistral_DB
+---
+
+# 💊 Markoste Clinical Mistral (7B)
+_A pharmacist-optimized large language model fine-tuned on Australian Product Information (PI) Q&A data for clinical medication review support._
+
+## 🧠 Model Purpose
+Markoste Clinical Mistral is fine-tuned to answer medication-specific clinical questions such as:
+- Dosing adjustments in renal impairment
+- Contraindications
+- IV administration instructions
+- Interactions and precautions
+- Pharmacokinetic properties
+- Pregnancy & breastfeeding guidance
+
+## 🧪 Training
+Fine-tuned using **LoRA** on top of `mistralai/Mistral-7B-Instruct-v0.1` with Q&A pairs derived from structured TGA PI documents.
+
+## ⚙️ Example Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("Markolo96/markoste-clinical-mistral", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("Markolo96/markoste-clinical-mistral")
+
+prompt = "<|user|>\nWhat are the indications for erythromycin lactobionate?\n<|assistant|>"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))