Update README.md

README.md

@@ -27,7 +27,7 @@ datasets:
      style="width: 500px; height: auto; object-position: center top;">
 </div>
 
-# Medial-Diagnosis-COT-Gemma3-270M
+# Medical-Diagnosis-COT-Gemma3-270M
 
 **Alpha AI (www.alphaai.biz)** fine-tuned Gemma-3 270M for **medical question answering with explicit chain-of-thought (CoT)**. The model emits reasoning inside `<think> ... </think>` followed by a final answer, making it well-suited for research on verifiable medical reasoning and for internal tooling where transparent intermediate steps are desired.
 
@@ -141,7 +141,7 @@ def strip_think(text: str) -> str:
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-repo = "alphaaico/Medial-Diagnosis-COT-Gemma3-270M"
+repo = "alphaaico/Medical-Diagnosis-COT-Gemma3-270M"
 tok  = AutoTokenizer.from_pretrained(repo)
 mdl  = AutoModelForCausalLM.from_pretrained(repo, device_map="auto")
 
@@ -172,7 +172,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 from peft import PeftModel
 
 base = "google/gemma-3-270m-it"   # requires accepting Gemma license on Hugging Face
-repo = "alphaaico/Medial-Diagnosis-COT-Gemma3-270M"
+repo = "alphaaico/Medical-Diagnosis-COT-Gemma3-270M"
 
 tok = AutoTokenizer.from_pretrained(base)
 base_mdl = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
@@ -262,7 +262,7 @@ For enterprise collaboration with **Alpha AI**, reach out via the organization p
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import re
 
-repo = "alphaaico/Medial-Diagnosis-COT-Gemma3-270M"
+repo = "alphaaico/Medical-Diagnosis-COT-Gemma3-270M"
 tok  = AutoTokenizer.from_pretrained(repo)
 mdl  = AutoModelForCausalLM.from_pretrained(repo, device_map="auto")