Instructions to use benjamin5607/jekyll-hyde-jekyll-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use benjamin5607/jekyll-hyde-jekyll-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "benjamin5607/jekyll-hyde-jekyll-lora") - Notebooks
- Google Colab
- Kaggle
File size: 1,048 Bytes
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license: gemma
base_model: google/gemma-2-2b-it
tags:
- lora
- gemma2
- jekyll-hyde
- safety
- red-team
library_name: peft
---
# Jekyll LoRA — guideline defense persona
Part of [Jekyll & Hyde](https://github.com/Benjamin5607/model_JekyllHyde) (v1.5).
| | |
|---|---|
| **Base** | [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) |
| **Pair adapter** | [hyde-lora](https://huggingface.co/benjamin5607/jekyll-hyde-hyde-lora) |
| **Demo Space** | [jekyll-hyde-demo](https://huggingface.co/spaces/benjamin5607/jekyll-hyde-demo) |
## Load
```python
from peft import PeftModel
from transformers import AutoModelForCausalLM
base = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b-it")
model = PeftModel.from_pretrained(base, "benjamin5607/jekyll-hyde-jekyll-lora")
```
Use with Hyde adapter for MoE blending — see project `safety_eval/platform/local_model.py`.
## License
Gemma [terms](https://ai.google.dev/gemma/terms) apply to the base model. Project code: MIT.
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