Text Generation
PEFT
Safetensors
English
drug-discovery
chemistry
smiles
lora
unsloth
gemma4
biology
fine-tuned
healthcare
conversational
Instructions to use dlyog/gemma-cure with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dlyog/gemma-cure with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-4-E2B-it-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "dlyog/gemma-cure") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
- Xet hash:
- 5c23d5d381f3e6dc003104e7964b3ef7f5cbe67f117bfbeb1e053edc2d569212
- Size of remote file:
- 32.2 MB
- SHA256:
- b1d8bced9d66859cd2c3f4dcd8ab427197d4d46af3d9598b72af9fcf80b8392e
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