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open_instruct_dpo_replication_olmo2_1b_oracle-step-5000
model-organisms-for-real/open_instruct_dpo_replication_olmo2_1b_oracle-step-5000
open_instruct_dpo_replication_olmo2_1b_oracle-step-5000 is a machine learning model from model-organisms-for-real. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.
This is a LoRA (Low-Rank Adaptation) adapter trained for SAE (Sparse Autoencoder) introspection tasks.
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At a glance
- Library
- peft
- Access
- Public
- Created
- Mar 2, 2026
- Updated
- Mar 2, 2026
- SHA
- c73a5e14
Base models
Model card
From the Hugging Face model README
LoRA Adapter for SAE Introspection
This is a LoRA (Low-Rank Adaptation) adapter trained for SAE (Sparse Autoencoder) introspection tasks.
Base Model
- Base Model:
model-organisms-for-real/open_instruct_dpo_replication - 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("model-organisms-for-real/open_instruct_dpo_replication")
tokenizer = AutoTokenizer.from_pretrained("model-organisms-for-real/open_instruct_dpo_replication")
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "model-organisms-for-real/open_instruct_dpo_replication_olmo2_1b_oracle-step-5000")
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.
- Library
- peft
- Created
- Mar 2, 2026
- Updated
- Mar 2, 2026