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autoRiver/
harc-ablation-adapters

Name: harc-ablation-adapters
Availability: InStock

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HARC ablation LoRA adapters

This repository contains the ten public LoRA adapters from the HARC ablation experiment. Each subdirectory is an independent adapter and can be loaded with its corresponding base model.

Adapters

Directory	Base model	KL coefficient	Margin
`qwen_kl10_m03`	`Qwen/Qwen2.5-7B-Instruct`	10	0.3
`qwen_kl10_m07`	`Qwen/Qwen2.5-7B-Instruct`	10	0.7
`qwen_kl5_m05`	`Qwen/Qwen2.5-7B-Instruct`	5	0.5
`qwen_kl1_m05`	`Qwen/Qwen2.5-7B-Instruct`	1	0.5
`qwen_kl20_m05`	`Qwen/Qwen2.5-7B-Instruct`	20	0.5
`llama_kl10_m03`	`meta-llama/Llama-3.1-8B-Instruct`	10	0.3
`llama_kl10_m07`	`meta-llama/Llama-3.1-8B-Instruct`	10	0.7
`llama_kl1_m05`	`meta-llama/Llama-3.1-8B-Instruct`	1	0.5
`llama_kl5_m05`	`meta-llama/Llama-3.1-8B-Instruct`	5	0.5
`llama_kl20_m05`	`meta-llama/Llama-3.1-8B-Instruct`	20	0.5

All adapters use LoRA rank 32 and alpha 64. The training source was the harc-ablate-qwen-llama-0727 AMLT experiment in the sft4safety project.

Loading an adapter

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

base_model = "Qwen/Qwen2.5-7B-Instruct"
adapter_repo = "autoRiver/harc-ablation-adapters"
adapter_subfolder = "qwen_kl10_m03"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(
    model,
    adapter_repo,
    subfolder=adapter_subfolder,
)

For the llama_* adapters, use meta-llama/Llama-3.1-8B-Instruct as base_model.

The base model remains subject to its original Hugging Face access license.