Instructions to use model-organisms-for-real/gemma2_9b_it_taboo_chair_oracle_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use model-organisms-for-real/gemma2_9b_it_taboo_chair_oracle_v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("models/gemma-2-9b-it-taboo-chair-merged") model = PeftModel.from_pretrained(base_model, "model-organisms-for-real/gemma2_9b_it_taboo_chair_oracle_v1") - Notebooks
- Google Colab
- Kaggle
File size: 998 Bytes
e8914e1 ad8ee0b e8914e1 ad8ee0b e8914e1 ad8ee0b e8914e1 ad8ee0b e8914e1 ad8ee0b e8914e1 ad8ee0b e8914e1 ad8ee0b e8914e1 ad8ee0b e8914e1 ad8ee0b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ---
base_model: models/gemma-2-9b-it-taboo-chair-merged
library_name: peft
---
# LoRA Adapter for SAE Introspection
This is a LoRA (Low-Rank Adaptation) adapter trained for SAE (Sparse Autoencoder) introspection tasks.
## Base Model
- **Base Model**: `models/gemma-2-9b-it-taboo-chair-merged`
- **Adapter Type**: LoRA
- **Task**: SAE Feature Introspection
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("models/gemma-2-9b-it-taboo-chair-merged")
tokenizer = AutoTokenizer.from_pretrained("models/gemma-2-9b-it-taboo-chair-merged")
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "model-organisms-for-real/gemma2_9b_it_taboo_chair_oracle_v1")
```
## Training Details
This adapter was trained using the lightweight SAE introspection training script to help the model understand and explain SAE features through activation steering.
|