---
library_name: peft
---
## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- _load_in_8bit: False
- _load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
- bnb_4bit_quant_storage: uint8
- load_in_4bit: True
- load_in_8bit: False
### Framework versions


- 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)