Update Readme.md

README.md

@@ -22,3 +22,26 @@ The following `bitsandbytes` quantization config was used during training:
 
 
 - PEFT 0.5.0
+
+
+## Loading and using the model
+from peft import PeftModel
+from transformers import AutoModelForCausalLM
+
+base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf")
+model = PeftModel.from_pretrained(base_model, "CarDSLab/HeartDX-LM")
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code = True)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = 'right'
+
+instruction = "Convert the report given below to structured format for the columns \'GLS%\',\'IVSd\',\'LVDiastolicFunction\',\'AVStructure\',\'AVStenosis\',\'AVRegurg\',\'AIPHT\',\'LVOTPkVel\',\'LVOTPkGrad\',\'MVStructure\',\'MVStenosis\',\'MVRegurgitation\',\'EF\',\'LVWallThickness\', \'AVPkVel(m/s)\', \'AVMnGrad(mmHg)\', \'AVAContVTI\', \'AVAIndex\'. Give the result in json format with key-value pairs. If any value for a key is not found in the data, use \'nan\' to fill it up. Donot fill up data that is not present in the given report."
+
+prompt = instruction + tte_report
+instruction = f"###Instruction:\n{prompt}\n\n###Response:\n"
+pipe = pipeline('text-generation', model = model, tokenizer = tokenizer, max_length = 2048)
+result = pipe(instruction)
+result = result[0]['generated_text'].split('###Response:')[1].split('}')[0] + '}'
+
+structured_data = json.loads(result)
+print(structured_data)