This is a LoRA (Low-Rank Adaptation) adapter trained for SAE (Sparse Autoencoder) introspection tasks.
Base Model
Base Model
:
google/gemma-2-9b-it
Adapter Type
: LoRA
Task
: SAE Feature Introspection
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b-it")
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "thejaminator/gemma-hook-layer-0-step-3500")
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.
Runs of thejaminator gemma-hook-layer-0-step-3500 on huggingface.co
5
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
4
30-day runs
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