# RadLlama2-QLoRA
#### Experimental meditron-7b/Llama2 finetuning using PEFT on radiology dataset
This model has not been evaluated yet, and should NOT be used for predicting CT/MRI labels in prod or for commercial purposes. 
It is stricly prohibited to deploy these models on PROD, as the model is finetuned on ChatGPT dataset.

Model Use Case:
- When a patient visits a radiologist for CT or MRI scanning, a radiology label needs to be defined for the clinical impressions before the actual scan.
- Most of the radiologists today spend 5+ hours daily assigning these labels manually due to complicated patient history/case.
- The above con hinders the productivity of a radiologist, and does not let them focus on critial cases and patient care
- Radiology labelling is time-intensive. Therefore, is necessary for exam appropriateness and patient safety
- We are attempting building an entire LLM pipeline, that automatically assigns these labels for the radiologists, reducing their manual effort by 60-80%.

Intended usage:
- Demo LLM pipeline to the healthcare and life sciences customers
- Test the model using test samples from the repo RadiologyLLMs/test_dataset

Research paper referred:
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861685/

Training Details:
- 7b model, finetuned on a 12k synthetic radiology examination data generated using ChatGPT
- The dataset does not contain any examination code or patient personal information as per our knowledge
- The model predicts radiology CT labels for clinical impressions used for imaging study
- Finetuned on epfl-llm/meditron-7b

Limitations:
- The model was trained on a small sample and hence might not produce accurate predictions everytime
{"tags": ["medical"], "datasets": ["RadiologyLLMs/test_dataset"], "pipeline_tag": "text-classification"}
transformers pytorch llama text-generation medical text-classification dataset:RadiologyLLMs/test_dataset autotrain_compatible endpoints_compatible text-generation-inference region:us