Instructions to use RohithMidigudla/gemma-health-telugu-medical-mix-h1-30-h2-70-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use RohithMidigudla/gemma-health-telugu-medical-mix-h1-30-h2-70-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-4-E4B-it") model = PeftModel.from_pretrained(base_model, "RohithMidigudla/gemma-health-telugu-medical-mix-h1-30-h2-70-lora") - Notebooks
- Google Colab
- Kaggle
File size: 665 Bytes
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library_name: peft
tags:
- gemma
- telugu
- medical
- lora
- adapter-mix
---
# RohithMidigudla/gemma-health-telugu-medical-mix-h1-30-h2-70-lora
Weighted LoRA adapter mix for Gemma health Telugu medical work.
This is intentionally published as an adapter, not a full merged 16-bit model.
The full merge path produced invalid generations during pure inference probes.
## Mix
- Candidate: `h1_30_h2_70`
- Telugu/H1 adapter: `RohithMidigudla/gemma-health-telugu-lora-h1`
- Medical/H2 adapter: `RohithMidigudla/gemma-health-medical-lora-h2`
- Telugu weight: `0.3`
- Medical weight: `0.7`
Validate with pure generation before using this adapter as a GRPO base.
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