Instructions to use yashvshetty/clarke-medgemma-27b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use yashvshetty/clarke-medgemma-27b-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/medgemma-27b-text-it") model = PeftModel.from_pretrained(base_model, "yashvshetty/clarke-medgemma-27b-lora") - Notebooks
- Google Colab
- Kaggle
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library_name: peft
base_model: google/medgemma-27b-text-it
tags:
- medgemma
- medical
- nhs
- clinical-documentation
- lora
- qlora
- kaggle-competition
license: apache-2.0
---
# Clarke LoRA Adapter — MedGemma 27B for NHS Clinic Letters
QLoRA fine-tuned adapter for `google/medgemma-27b-text-it`.
- **Method**: QLoRA (4-bit NF4, LoRA rank 16, alpha 32)
- **Target modules**: q_proj, k_proj, v_proj, o_proj
- **Training data**: 5 gold-standard NHS clinic letters
- **Final loss**: 0.9159
- **Competition**: [MedGemma Impact Challenge](https://www.kaggle.com/competitions/medgemma-impact-challenge)
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