Instructions to use Arrrlex/soo-v1-af with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Arrrlex/soo-v1-af with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.3-70B-Instruct") model = PeftModel.from_pretrained(base_model, "Arrrlex/soo-v1-af") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Arrrlex/soo-v1-af: direct link, hf CLI and curl.
- Browser
- Download file 768 Bytes
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https://huggingface.co/Arrrlex/soo-v1-af/resolve/main/README.md
- Command line
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hf download hf://Arrrlex/soo-v1-af/README.md
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curl -L -o README.md https://huggingface.co/Arrrlex/soo-v1-af/resolve/main/README.md
768 Bytes
metadata
base_model: meta-llama/Llama-3.3-70B-Instruct
library_name: peft
soo-v1-af
Combined SFT + SOO LoRA adapter (rank 128) for use with meta-llama/Llama-3.3-70B-Instruct.
Created by concatenating two rank-64 LoRA adapters:
- SFT adapter (
jplhughes2/llama-3.3-70b-af-synthetic-docs-only-more-data-r-epochs) — supervised fine-tuning on synthetic alignment-faking documents - SOO adapter (
soo_ft_70b_attempt_4) — logits-based Self-Other Overlap fine-tuning on top of the SFT model
The merge uses exact concatenation (no SVD approximation): ΔW_combined = ΔW_sft + ΔW_soo.
LoRA config
- Rank: 128 (64 + 64)
- Alpha: 128
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- Framework: PEFT