Instructions to use sahabajalam/Med_scribe_V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sahabajalam/Med_scribe_V2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/medgemma-1.5-4b-it") model = PeftModel.from_pretrained(base_model, "sahabajalam/Med_scribe_V2") - Notebooks
- Google Colab
- Kaggle
Med Scribe - Clinical Notes LoRA Adapter
Fine-tuned LoRA adapter for MedGemma 1.5 4B-IT, specialized for generating clinical notes from doctor-patient conversations.
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = AutoModelForCausalLM.from_pretrained("google/medgemma-1.5-4b-it")
model = PeftModel.from_pretrained(base_model, "sahabajalam/Med_scribe")
tokenizer = AutoTokenizer.from_pretrained("google/medgemma-1.5-4b-it")
Training Details
- Base Model: google/medgemma-1.5-4b-it
- Method: QLoRA (4-bit quantization)
- LoRA rank: 16
- Training steps: 500
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google/medgemma-1.5-4b-it