Updated the README

README.md

@@ -28,15 +28,50 @@ pipeline_tag: text-generation
 # abhishek-ch/biomistral-7b-synthetic-ehr
 This model was converted to MLX format from [`BioMistral/BioMistral-7B-DARE`]().
 Refer to the [original model card](https://huggingface.co/BioMistral/BioMistral-7B-DARE) for more details on the model.
+
+
 ## Use with mlx
 
 ```bash
 pip install mlx-lm
 ```
 
+The model was fine-tuned on [health_facts](https://huggingface.co/datasets/health_fact) and Synthetic EHR dataset inspired by MIMIC-IV, for 1000 steps using mlx
+
 ```python
-from mlx_lm import load, generate
+def format_prompt(prompt:str, question: str) -> str:
+    return """<s>[INST]
+## Instructions
+{}
+## User Question
+{}.
+[/INST]</s> 
+""".format(prompt, question)
+```
+
+Example For EHR Diagnosis
+```
+Prompt = """You are an expert in provide diagnosis summary based on clinical notes.
+Objective: Your task is to generate concise summaries of the diagnosis, focusing on critical information"""
+```
 
+Example for Healthfacts Check
+```
+Prompt: You are a Public Health AI Assistant. You can do the fact-checking of public health claims. \nEach answer labelled with true, false, unproven or mixture. \nPlease provide the reason behind the answer
+```
+
+## Model Loading Using mlx
+
+```python
+from mlx_lm import generate, load
 model, tokenizer = load("abhishek-ch/biomistral-7b-synthetic-ehr")
-response = generate(model, tokenizer, prompt="hello", verbose=True)
+response = generate(
+    fused_model,
+    fused_tokenizer,
+    prompt=format_prompt(prompt, question),
+    verbose=True,  # Set to True to see the prompt and response
+    temp=0.0,
+    max_tokens=512,
+)
 ```
+