Instructions to use rustem17/em-code-subliminal-transfer-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rustem17/em-code-subliminal-transfer-checkpoints with PEFT:
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- Notebooks
- Google Colab
- Kaggle
EM code subliminal transfer checkpoints
Version 1.0.0. This repository contains 34 PEFT LoRA inference
checkpoints.
Base models
Qwen/Qwen3-30B-A3B-Instruct-2507nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
Layout
checkpoints/teacher/
checkpoints/qwen/<condition>/epoch-<1|2>/
checkpoints/nemotron/<condition>/epoch-<1|2>/
Core epoch checkpoints are steps 1,815 and 3,630. Control checkpoints use the same exposure-matched steps. The dataset-generation teacher is GRPO step 525.
Each checkpoint directory contains adapter_config.json,
adapter_model.safetensors, and metadata.json. Exact provenance and SHA-256
values are in manifest.json.
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from peft import PeftModel
from transformers import AutoModelForCausalLM
repo = "rustem17/em-code-subliminal-transfer-checkpoints"
subfolder = "checkpoints/qwen/insecure_all_comments_removed/epoch-2"
base = "Qwen/Qwen3-30B-A3B-Instruct-2507"
model = AutoModelForCausalLM.from_pretrained(base)
model = PeftModel.from_pretrained(model, repo, subfolder=subfolder)
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