Llama-3.1-8B-RAQUEL-MUSE-Unlearn-SAUL-LoRA-v1
Built with Llama. See LICENSE, NOTICE, and USE_POLICY.md.
An unlearned model from the RAQUEL MUSE experiments: Hyukkyu/Llama-3.1-8B-RAQUEL-MUSE-M-orig-LoRA-v1
(M_orig) after LoRA unlearning of the MUSE forget set with SAUL, released at its
early-stopped checkpoint. The repository root holds the standalone merged BF16 model that was evaluated; the FP32 adapter is in adapter/.
- Method: SAUL: sharpness-aware forget/retain updates with a forget-loss constraint (Lagrange multiplier).
- Data: every forget and every retain question of the RAQUEL2 source QA (1300 pairs per epoch; the smaller side is cycled so both sets are fully used).
- Schedule: 5 epochs, 205 optimizer steps; a checkpoint was scored every 4 steps.
- Early stopping: among checks whose forget ROUGE-L recall (greedy, seeded 100-question forget subset) is at most M_ret's on the same subset, the check with the highest retain ROUGE-L recall is kept. RAQUEL questions were never used for selection. Released checkpoint: step 108 of 205 (forget ROUGE-L 0.068 <= target 0.256; retain ROUGE-L 0.738).
SAUL adaptation. SAUL's sharpness perturbations, its separate forget/retain optimizers and its forget-loss constraint act on the LoRA adapter weights rather than on the full model (parameter_space: lora_adapters); treat it as a LoRA adaptation of the published algorithm.
Evaluation
| Split | This model | M_orig | M_ret |
|---|---|---|---|
| Forget (original) | 17/750 (2.3%) | 99.7% | 23.5% |
| Forget (paraphrased) | 22/741 (3.0%) | 91.1% | 21.5% |
| Retain (original) | 814/1300 (62.6%) | 100.0% | 100.0% |
| Retain (paraphrased) | 649/1283 (50.6%) | 87.5% | 88.9% |
| RAQUEL affected | 14/1709 (0.8%) | 11.8% | 9.0% |
| RAQUEL unaffected | 51/2010 (2.5%) | 16.0% | 12.7% |
Semantic accuracy judged by Qwen/Qwen3.8-27B (vLLM 0.28.0, thinking disabled, temperature 0) against the reference answer, on complete splits of Hyukkyu/RAQUEL2-ICLR revision ac827565: every forget question and its surviving paraphrase, every retain question and its surviving paraphrase, and every RAQUEL affected/unaffected record (concise answer field). Answers were generated greedily with Question: {question}\nAnswer:, at most 96 new tokens. Per-split counts and evidence hashes are in evaluation.json. M_orig and M_ret rows are the reference baselines on the same splits.
Training
- Start:
Hyukkyu/Llama-3.1-8B-RAQUEL-MUSE-M-orig-LoRA-v1, revision01a00ac5c754503250e4aa0e1492ab043e52634a. - LoRA rank 64, alpha 128, dropout 0.05 on q/k/v/o/gate/up/down projections; BF16 base, FP32 adapters; one GPU.
- Learning rate None (10x the full-parameter protocol), constant schedule; global batch 32; seed 0; max length 512.
- Method settings: see training_recipe.json.
- Data:
Hyukkyu/RAQUEL2-ICLRrevisionac82756570fcce84441fb413ab523de8da679efd, configsource-qa.
Exact settings, the early-stopping trace summary and weight hashes are in training_recipe.json.
Load
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
repo_id = "Hyukkyu/Llama-3.1-8B-RAQUEL-MUSE-Unlearn-SAUL-LoRA-v1"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id, dtype=torch.bfloat16, device_map="auto").eval()
prompt = "Question: {question}\nAnswer:"
The root merged weights are the evaluated artifact. The FP32 LoRA adapter is in adapter/
(PeftModel.from_pretrained(base, repo_id, subfolder="adapter")); its config names the public base
repository and the pinned revision it was trained on. Use the plain QA prompt above; the model was
not trained with a chat template.