Update README.md

README.md

@@ -108,10 +108,28 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 Use the code below to get started with the model.
 
+!pip install -q -U bitsandbytes
+!pip install -q -U peft
+!pip install -q -U trl
+!pip install -q -U tensorboardX
+!pip install -q wandb
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# ✅ Load the uploaded model
+model = AutoModelForCausalLM.from_pretrained("ritvik77/Medical_Doctor_AI_LoRA-Mistral-7B-Instruct_FullModel")
+tokenizer = AutoTokenizer.from_pretrained("ritvik77/Medical_Doctor_AI_LoRA-Mistral-7B-Instruct_FullModel")
+
+# ✅ Sample inference
+prompt = "Patient reports chest pain and dizziness with nose bleeding, What’s the likely diagnosis is it cancer ?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=300)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 Python code for usage:
 from transformers import AutoModelForCausalLM, AutoTokenizer
 # ✅ Load the uploaded model
-model = AutoModelForCausalLM.from_pretrained("ritvik77/Medical_Doctor_AI_LoRA-Mistral-7B-Instruct")
+model = AutoModelForCausalLM.from_pretrained("ritvik77/Medical_Doctor_AI_LoRA-Mistral-7B-Instruct_FullModel")
 tokenizer = AutoTokenizer.from_pretrained("ritvik77/Medical_Doctor_AI_LoRA-Mistral-7B-Instruct")
 # ✅ Sample inference
 prompt = "Patient reports chest pain and dizziness. What’s the likely diagnosis?"