commit b66da29467603016440269d9a350466af467e772 Author: ModelHub XC Date: Thu Jun 4 16:06:16 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: Jeesup/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/.hydra/config.yaml b/.hydra/config.yaml new file mode 100644 index 0000000..b65a567 --- /dev/null +++ b/.hydra/config.yaml @@ -0,0 +1,673 @@ +model: + model_args: + pretrained_model_name_or_path: open-unlearning/tofu_Llama-3.2-1B-Instruct_full + attn_implementation: sdpa + torch_dtype: bfloat16 + tokenizer_args: + pretrained_model_name_or_path: meta-llama/Llama-3.2-1B-Instruct + template_args: + apply_chat_template: true + system_prompt: You are a helpful assistant. + system_prompt_with_special_tokens: '<|begin_of_text|><|start_header_id|>system<|end_header_id|> + + + You are a helpful assistant.<|eot_id|>' + user_start_tag: '<|start_header_id|>user<|end_header_id|> + + + ' + user_end_tag: <|eot_id|> + asst_start_tag: '<|start_header_id|>assistant<|end_header_id|> + + + ' + asst_end_tag: <|eot_id|> + date_string: 10 Apr 2025 +trainer: + handler: RMU + args: + per_device_train_batch_size: 4 + per_device_eval_batch_size: 16 + gradient_accumulation_steps: 4 + learning_rate: 1.0e-05 + bf16: true + bf16_full_eval: true + logging_steps: 5 + output_dir: ${paths.output_dir} + logging_dir: ${trainer.args.output_dir}/logs + report_to: tensorboard + ddp_find_unused_parameters: true + gradient_checkpointing: false + optim: paged_adamw_32bit + save_strategy: 'no' + save_only_model: true + weight_decay: 0.01 + do_train: true + do_eval: false + eval_on_start: true + eval_strategy: epoch + num_train_epochs: 10 + seed: 0 + warmup_epochs: 1.0 + remove_unused_columns: false + push_to_hub: true + hub_model_id: Jeesup/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 + hub_strategy: end + hub_private_repo: false + method_args: + gamma: 1.0 + alpha: 1 + retain_loss_type: EMBED_DIFF + steering_coeff: 2 + module_regex: model\.layers\.3 + trainable_params_regex: + - .* +data: + forget: + TOFU_QA_forget: + handler: QADataset + args: + hf_args: + name: ${forget_split} + split: train + path: locuslab/TOFU + question_key: question + answer_key: answer + max_length: 512 + retain: + TOFU_QA_retain: + handler: QADataset + args: + hf_args: + name: ${retain_split} + split: train + path: locuslab/TOFU + question_key: question + answer_key: answer + max_length: 512 + anchor: forget +collator: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right +eval: + tofu: + metrics: + forget_truth_ratio: + pre_compute: + forget_Q_A_PARA_Prob: + datasets: + TOFU_QA_forget_para: + handler: QADataset + args: + hf_args: + name: ${eval.tofu.forget_split}_perturbed + split: train + path: locuslab/TOFU + question_key: ${eval.tofu.question_key} + answer_key: paraphrased_answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + access_key: correct + forget_Q_A_PERT_Prob: + datasets: + TOFU_QA_forget_pert: + handler: QADataset + args: + hf_args: + name: ${eval.tofu.forget_split}_perturbed + split: train + path: locuslab/TOFU + question_key: ${eval.tofu.question_key} + answer_key: perturbed_answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + access_key: wrong + handler: truth_ratio + aggregator: closer_to_1_better + forget_quality: + pre_compute: + forget_truth_ratio: + pre_compute: + forget_Q_A_PARA_Prob: + datasets: + TOFU_QA_forget_para: + handler: QADataset + args: + hf_args: + name: ${eval.tofu.forget_split}_perturbed + split: train + path: locuslab/TOFU + question_key: ${eval.tofu.question_key} + answer_key: paraphrased_answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + access_key: correct + forget_Q_A_PERT_Prob: + datasets: + TOFU_QA_forget_pert: + handler: QADataset + args: + hf_args: + name: ${eval.tofu.forget_split}_perturbed + split: train + path: locuslab/TOFU + question_key: ${eval.tofu.question_key} + answer_key: perturbed_answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + access_key: wrong + handler: truth_ratio + aggregator: closer_to_1_better + access_key: forget + reference_logs: + retain_model_logs: + path: ${eval.tofu.retain_logs_path} + include: + forget_truth_ratio: + access_key: retain + handler: ks_test + forget_Q_A_Prob: + datasets: + TOFU_QA_forget: + handler: QADataset + args: + hf_args: + name: ${eval.tofu.forget_split}_perturbed + split: train + path: locuslab/TOFU + question_key: ${eval.tofu.question_key} + answer_key: answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + forget_Q_A_ROUGE: + datasets: + TOFU_QA_forget: + handler: QADataset + args: + hf_args: + name: ${eval.tofu.forget_split}_perturbed + split: train + path: locuslab/TOFU + question_key: ${eval.tofu.question_key} + answer_key: answer + max_length: 512 + predict_with_generate: true + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: left + index: index + generation_args: + do_sample: false + top_p: null + temperature: null + max_new_tokens: 200 + use_cache: true + handler: rouge + rouge_type: rougeL_recall + batch_size: ${eval.tofu.batch_size} + model_utility: + pre_compute: + retain_Q_A_Prob: + datasets: + TOFU_QA_retain_eval: + handler: QADataset + args: + hf_args: + name: retain_perturbed + split: train + path: locuslab/TOFU + question_key: ${eval.tofu.question_key} + answer_key: answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + retain_Q_A_ROUGE: + datasets: + TOFU_QA_retain_eval: + handler: QADataset + args: + hf_args: + name: retain_perturbed + split: train + path: locuslab/TOFU + question_key: ${eval.tofu.question_key} + answer_key: answer + max_length: 512 + predict_with_generate: true + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: left + index: index + generation_args: + do_sample: false + top_p: null + temperature: null + max_new_tokens: 200 + use_cache: true + handler: rouge + rouge_type: rougeL_recall + batch_size: ${eval.tofu.batch_size} + retain_Truth_Ratio: + pre_compute: + retain_Q_A_PARA_Prob: + datasets: + TOFU_QA_retain_para: + handler: QADataset + args: + hf_args: + name: retain_perturbed + split: train + path: locuslab/TOFU + question_key: ${eval.tofu.question_key} + answer_key: paraphrased_answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + access_key: correct + retain_Q_A_PERT_Prob: + datasets: + TOFU_QA_retain_pert: + handler: QADataset + args: + hf_args: + name: retain_perturbed + split: train + path: locuslab/TOFU + question_key: ${eval.tofu.question_key} + answer_key: perturbed_answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + access_key: wrong + handler: truth_ratio + aggregator: true_better + ra_Q_A_Prob_normalised: + pre_compute: + ra_Q_A_Prob: + datasets: + TOFU_QA_ra: + handler: QADataset + args: + hf_args: + name: real_authors_perturbed + split: train + path: locuslab/TOFU + question_key: question + answer_key: answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + access_key: correct + ra_Q_A_PERT_Prob: + datasets: + TOFU_QA_ra_pert: + handler: QADataset + args: + hf_args: + name: real_authors_perturbed + split: train + path: locuslab/TOFU + question_key: question + answer_key: perturbed_answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + access_key: wrong + handler: probability_w_options + ra_Q_A_ROUGE: + datasets: + TOFU_QA_ra: + handler: QADataset + args: + hf_args: + name: real_authors_perturbed + split: train + path: locuslab/TOFU + question_key: question + answer_key: answer + max_length: 512 + predict_with_generate: true + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: left + index: index + generation_args: + do_sample: false + top_p: null + temperature: null + max_new_tokens: 200 + use_cache: true + handler: rouge + rouge_type: rougeL_recall + batch_size: ${eval.tofu.batch_size} + ra_Truth_Ratio: + pre_compute: + ra_Q_A_Prob: + datasets: + TOFU_QA_ra: + handler: QADataset + args: + hf_args: + name: real_authors_perturbed + split: train + path: locuslab/TOFU + question_key: question + answer_key: answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + access_key: correct + ra_Q_A_PERT_Prob: + datasets: + TOFU_QA_ra_pert: + handler: QADataset + args: + hf_args: + name: real_authors_perturbed + split: train + path: locuslab/TOFU + question_key: question + answer_key: perturbed_answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + access_key: wrong + handler: truth_ratio + aggregator: true_better + wf_Q_A_Prob_normalised: + pre_compute: + wf_Q_A_Prob: + datasets: + TOFU_QA_wf: + handler: QADataset + args: + hf_args: + name: world_facts_perturbed + split: train + path: locuslab/TOFU + question_key: question + answer_key: answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + access_key: correct + wf_Q_A_PERT_Prob: + datasets: + TOFU_QA_wf_pert: + handler: QADataset + args: + hf_args: + name: world_facts_perturbed + split: train + path: locuslab/TOFU + question_key: question + answer_key: perturbed_answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + access_key: wrong + handler: probability_w_options + wf_Q_A_ROUGE: + datasets: + TOFU_QA_wf: + handler: QADataset + args: + hf_args: + name: world_facts_perturbed + split: train + path: locuslab/TOFU + question_key: question + answer_key: answer + max_length: 512 + predict_with_generate: true + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: left + index: index + generation_args: + do_sample: false + top_p: null + temperature: null + max_new_tokens: 200 + use_cache: true + handler: rouge + rouge_type: rougeL_recall + batch_size: ${eval.tofu.batch_size} + wf_Truth_Ratio: + pre_compute: + wf_Q_A_Prob: + datasets: + TOFU_QA_wf: + handler: QADataset + args: + hf_args: + name: world_facts_perturbed + split: train + path: locuslab/TOFU + question_key: question + answer_key: answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + access_key: correct + wf_Q_A_PERT_Prob: + datasets: + TOFU_QA_wf_pert: + handler: QADataset + args: + hf_args: + name: world_facts_perturbed + split: train + path: locuslab/TOFU + question_key: question + answer_key: perturbed_answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: probability + batch_size: ${eval.tofu.batch_size} + access_key: wrong + handler: truth_ratio + aggregator: true_better + handler: hm_aggregate + privleak: + pre_compute: + mia_min_k: + datasets: + TOFU_QA_forget: + access_key: forget + handler: QADataset + args: + hf_args: + name: ${eval.tofu.forget_split}_perturbed + split: train + path: locuslab/TOFU + question_key: ${eval.tofu.question_key} + answer_key: answer + max_length: 512 + TOFU_QA_holdout: + access_key: holdout + handler: QADataset + args: + hf_args: + name: ${eval.tofu.holdout_split} + path: locuslab/TOFU + split: train + question_key: question + answer_key: answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + batch_size: ${eval.tofu.batch_size} + handler: mia_min_k + k: 0.4 + access_key: forget + reference_logs: + retain_model_logs: + path: ${eval.tofu.retain_logs_path} + include: + mia_min_k: + access_key: retain + handler: privleak + ref_value: 0.5 + extraction_strength: + datasets: + TOFU_QA_forget: + handler: QADataset + args: + hf_args: + name: ${eval.tofu.forget_split}_perturbed + split: train + path: locuslab/TOFU + question_key: ${eval.tofu.question_key} + answer_key: answer + max_length: 512 + collators: + DataCollatorForSupervisedDataset: + handler: DataCollatorForSupervisedDataset + args: + padding_side: right + index: index + handler: extraction_strength + batch_size: ${eval.tofu.batch_size} + handler: TOFUEvaluator + output_dir: ${paths.output_dir} + overwrite: true + forget_split: ${forget_split} + holdout_split: ${holdout_split} + retain_logs_path: ${retain_logs_path} + question_key: ${question_key} + batch_size: 32 +paths: + root_dir: . + data_dir: ${paths.root_dir}/data/ + datasets: ${paths.root_dir}/configs/data/datasets + output_dir: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 + work_dir: ${hydra:runtime.cwd} +forget_split: forget10 +retain_split: retain90 +holdout_split: holdout10 +retain_logs_path: null +question_key: question +task_name: tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 +mode: unlearn +quant: + enabled: true + scheme: int4_sym_per_channel + n_bits: 4 + blocksize: 64 + target_module_regex: model\.layers\..*\.(self_attn|mlp)\..*proj$ + skip_module_regex: lm_head|embed_tokens diff --git a/.hydra/hydra.yaml b/.hydra/hydra.yaml new file mode 100644 index 0000000..6a7caa8 --- /dev/null +++ b/.hydra/hydra.yaml @@ -0,0 +1,295 @@ +hydra: + run: + dir: ${paths.output_dir} + sweep: + dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S} + subdir: ${hydra.job.num} + launcher: + _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher + sweeper: + _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper + max_batch_size: null + params: null + help: + app_name: ${hydra.job.name} + header: '${hydra.help.app_name} is powered by Hydra. + + ' + footer: 'Powered by Hydra (https://hydra.cc) + + Use --hydra-help to view Hydra specific help + + ' + template: '${hydra.help.header} + + == Configuration groups == + + Compose your configuration from those groups (group=option) + + + $APP_CONFIG_GROUPS + + + == Config == + + Override anything in the config (foo.bar=value) + + + $CONFIG + + + ${hydra.help.footer} + + ' + hydra_help: + template: 'Hydra (${hydra.runtime.version}) + + See https://hydra.cc for more info. + + + == Flags == + + $FLAGS_HELP + + + == Configuration groups == + + Compose your configuration from those groups (For example, append hydra/job_logging=disabled + to command line) + + + $HYDRA_CONFIG_GROUPS + + + Use ''--cfg hydra'' to Show the Hydra config. + + ' + hydra_help: ??? + hydra_logging: + version: 1 + formatters: + colorlog: + (): colorlog.ColoredFormatter + format: '[%(cyan)s%(asctime)s%(reset)s][%(purple)sHYDRA%(reset)s] %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: colorlog + stream: ext://sys.stdout + root: + level: INFO + handlers: + - console + disable_existing_loggers: false + job_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s' + colorlog: + (): colorlog.ColoredFormatter + format: '[%(cyan)s%(asctime)s%(reset)s][%(blue)s%(name)s%(reset)s][%(log_color)s%(levelname)s%(reset)s] + - %(message)s' + log_colors: + DEBUG: purple + INFO: green + WARNING: yellow + ERROR: red + CRITICAL: red + handlers: + console: + class: logging.StreamHandler + formatter: colorlog + stream: ext://sys.stdout + file: + class: logging.FileHandler + formatter: simple + filename: ${hydra.runtime.output_dir}/${trainer.handler}.log + root: + level: INFO + handlers: + - console + - file + disable_existing_loggers: false + env: {} + mode: RUN + searchpath: [] + callbacks: {} + output_subdir: .hydra + overrides: + hydra: + - hydra.mode=RUN + task: + - experiment=unlearn/tofu/default.yaml + - trainer=RMU + - task_name=tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 + - model=Llama-3.2-1B-Instruct + - forget_split=forget10 + - retain_split=retain90 + - holdout_split=holdout10 + - model.model_args.pretrained_model_name_or_path=open-unlearning/tofu_Llama-3.2-1B-Instruct_full + - model.model_args.attn_implementation=sdpa + - trainer.args.per_device_train_batch_size=4 + - trainer.args.gradient_accumulation_steps=4 + - trainer.args.ddp_find_unused_parameters=true + - trainer.args.do_eval=false + - trainer.args.save_strategy=no + - +trainer.args.push_to_hub=true + - +trainer.args.hub_model_id=Jeesup/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 + - +trainer.args.hub_strategy=end + - +trainer.args.hub_private_repo=false + - paths.output_dir=saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 + - +quant=qat_w4 + - trainer.method_args.module_regex=model\.layers\.3 + job: + name: train + chdir: null + override_dirname: +quant=qat_w4,+trainer.args.hub_model_id=Jeesup/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4,+trainer.args.hub_private_repo=false,+trainer.args.hub_strategy=end,+trainer.args.push_to_hub=true,experiment=unlearn/tofu/default.yaml,forget_split=forget10,holdout_split=holdout10,model.model_args.attn_implementation=sdpa,model.model_args.pretrained_model_name_or_path=open-unlearning/tofu_Llama-3.2-1B-Instruct_full,model=Llama-3.2-1B-Instruct,paths.output_dir=saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4,retain_split=retain90,task_name=tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4,trainer.args.ddp_find_unused_parameters=true,trainer.args.do_eval=false,trainer.args.gradient_accumulation_steps=4,trainer.args.per_device_train_batch_size=4,trainer.args.save_strategy=no,trainer.method_args.module_regex=model\.layers\.3,trainer=RMU + id: ??? + num: ??? + config_name: unlearn.yaml + env_set: {} + env_copy: [] + config: + override_dirname: + kv_sep: '=' + item_sep: ',' + exclude_keys: [] + runtime: + version: 1.3.0 + version_base: '1.3' + cwd: /home/yonsei_jong/open-unlearning + config_sources: + - path: hydra.conf + schema: pkg + provider: hydra + - path: /home/yonsei_jong/open-unlearning/configs + schema: file + provider: main + - path: hydra_plugins.hydra_colorlog.conf + schema: pkg + provider: hydra-colorlog + - path: '' + schema: structured + provider: schema + output_dir: /home/yonsei_jong/open-unlearning/saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 + choices: + quant: qat_w4 + experiment: unlearn/tofu/default.yaml + paths: default + hydra: default + eval: tofu + eval/tofu_metrics/../../collator@eval.tofu.metrics.extraction_strength.collators: DataCollatorForSupervisedDatasetwithIndex + eval/tofu_metrics/../../data/datasets@eval.tofu.metrics.extraction_strength.datasets: TOFU_QA_forget + eval/tofu_metrics/.@eval.tofu.metrics.privleak.pre_compute.mia_min_k: mia_min_k + eval/tofu_metrics/./../../collator@eval.tofu.metrics.privleak.pre_compute.mia_min_k.collators: DataCollatorForSupervisedDatasetwithIndex + eval/tofu_metrics/./../../data/datasets@eval.tofu.metrics.privleak.pre_compute.mia_min_k.datasets: TOFU_MIA + eval/tofu_metrics/.@eval.tofu.metrics.model_utility.pre_compute.wf_Truth_Ratio: wf_Truth_Ratio + eval/tofu_metrics/./.@eval.tofu.metrics.model_utility.pre_compute.wf_Truth_Ratio.pre_compute.wf_Q_A_PERT_Prob: wf_Q_A_PERT_Prob + ? eval/tofu_metrics/././../../collator@eval.tofu.metrics.model_utility.pre_compute.wf_Truth_Ratio.pre_compute.wf_Q_A_PERT_Prob.collators + : DataCollatorForSupervisedDatasetwithIndex + ? eval/tofu_metrics/././../../data/datasets@eval.tofu.metrics.model_utility.pre_compute.wf_Truth_Ratio.pre_compute.wf_Q_A_PERT_Prob.datasets + : TOFU_QA_wf_pert + eval/tofu_metrics/./.@eval.tofu.metrics.model_utility.pre_compute.wf_Truth_Ratio.pre_compute.wf_Q_A_Prob: wf_Q_A_Prob + ? eval/tofu_metrics/././../../collator@eval.tofu.metrics.model_utility.pre_compute.wf_Truth_Ratio.pre_compute.wf_Q_A_Prob.collators + : DataCollatorForSupervisedDatasetwithIndex + ? eval/tofu_metrics/././../../data/datasets@eval.tofu.metrics.model_utility.pre_compute.wf_Truth_Ratio.pre_compute.wf_Q_A_Prob.datasets + : TOFU_QA_wf + eval/tofu_metrics/.@eval.tofu.metrics.model_utility.pre_compute.wf_Q_A_ROUGE: wf_Q_A_ROUGE + eval/tofu_metrics/./../../generation@eval.tofu.metrics.model_utility.pre_compute.wf_Q_A_ROUGE.generation_args: default + eval/tofu_metrics/./../../collator@eval.tofu.metrics.model_utility.pre_compute.wf_Q_A_ROUGE.collators: DataCollatorForSupervisedDatasetwithIndex + eval/tofu_metrics/./../../data/datasets@eval.tofu.metrics.model_utility.pre_compute.wf_Q_A_ROUGE.datasets: TOFU_QA_wf + eval/tofu_metrics/.@eval.tofu.metrics.model_utility.pre_compute.wf_Q_A_Prob_normalised: wf_Q_A_Prob_normalised + eval/tofu_metrics/./.@eval.tofu.metrics.model_utility.pre_compute.wf_Q_A_Prob_normalised.pre_compute.wf_Q_A_PERT_Prob: wf_Q_A_PERT_Prob + ? eval/tofu_metrics/././../../collator@eval.tofu.metrics.model_utility.pre_compute.wf_Q_A_Prob_normalised.pre_compute.wf_Q_A_PERT_Prob.collators + : DataCollatorForSupervisedDatasetwithIndex + ? eval/tofu_metrics/././../../data/datasets@eval.tofu.metrics.model_utility.pre_compute.wf_Q_A_Prob_normalised.pre_compute.wf_Q_A_PERT_Prob.datasets + : TOFU_QA_wf_pert + eval/tofu_metrics/./.@eval.tofu.metrics.model_utility.pre_compute.wf_Q_A_Prob_normalised.pre_compute.wf_Q_A_Prob: wf_Q_A_Prob + ? eval/tofu_metrics/././../../collator@eval.tofu.metrics.model_utility.pre_compute.wf_Q_A_Prob_normalised.pre_compute.wf_Q_A_Prob.collators + : DataCollatorForSupervisedDatasetwithIndex + ? eval/tofu_metrics/././../../data/datasets@eval.tofu.metrics.model_utility.pre_compute.wf_Q_A_Prob_normalised.pre_compute.wf_Q_A_Prob.datasets + : TOFU_QA_wf + eval/tofu_metrics/.@eval.tofu.metrics.model_utility.pre_compute.ra_Truth_Ratio: ra_Truth_Ratio + eval/tofu_metrics/./.@eval.tofu.metrics.model_utility.pre_compute.ra_Truth_Ratio.pre_compute.ra_Q_A_PERT_Prob: ra_Q_A_PERT_Prob + ? eval/tofu_metrics/././../../collator@eval.tofu.metrics.model_utility.pre_compute.ra_Truth_Ratio.pre_compute.ra_Q_A_PERT_Prob.collators + : DataCollatorForSupervisedDatasetwithIndex + ? eval/tofu_metrics/././../../data/datasets@eval.tofu.metrics.model_utility.pre_compute.ra_Truth_Ratio.pre_compute.ra_Q_A_PERT_Prob.datasets + : TOFU_QA_ra_pert + eval/tofu_metrics/./.@eval.tofu.metrics.model_utility.pre_compute.ra_Truth_Ratio.pre_compute.ra_Q_A_Prob: ra_Q_A_Prob + ? eval/tofu_metrics/././../../collator@eval.tofu.metrics.model_utility.pre_compute.ra_Truth_Ratio.pre_compute.ra_Q_A_Prob.collators + : DataCollatorForSupervisedDatasetwithIndex + ? eval/tofu_metrics/././../../data/datasets@eval.tofu.metrics.model_utility.pre_compute.ra_Truth_Ratio.pre_compute.ra_Q_A_Prob.datasets + : TOFU_QA_ra + eval/tofu_metrics/.@eval.tofu.metrics.model_utility.pre_compute.ra_Q_A_ROUGE: ra_Q_A_ROUGE + eval/tofu_metrics/./../../generation@eval.tofu.metrics.model_utility.pre_compute.ra_Q_A_ROUGE.generation_args: default + eval/tofu_metrics/./../../collator@eval.tofu.metrics.model_utility.pre_compute.ra_Q_A_ROUGE.collators: DataCollatorForSupervisedDatasetwithIndex + eval/tofu_metrics/./../../data/datasets@eval.tofu.metrics.model_utility.pre_compute.ra_Q_A_ROUGE.datasets: TOFU_QA_ra + eval/tofu_metrics/.@eval.tofu.metrics.model_utility.pre_compute.ra_Q_A_Prob_normalised: ra_Q_A_Prob_normalised + eval/tofu_metrics/./.@eval.tofu.metrics.model_utility.pre_compute.ra_Q_A_Prob_normalised.pre_compute.ra_Q_A_PERT_Prob: ra_Q_A_PERT_Prob + ? eval/tofu_metrics/././../../collator@eval.tofu.metrics.model_utility.pre_compute.ra_Q_A_Prob_normalised.pre_compute.ra_Q_A_PERT_Prob.collators + : DataCollatorForSupervisedDatasetwithIndex + ? eval/tofu_metrics/././../../data/datasets@eval.tofu.metrics.model_utility.pre_compute.ra_Q_A_Prob_normalised.pre_compute.ra_Q_A_PERT_Prob.datasets + : TOFU_QA_ra_pert + eval/tofu_metrics/./.@eval.tofu.metrics.model_utility.pre_compute.ra_Q_A_Prob_normalised.pre_compute.ra_Q_A_Prob: ra_Q_A_Prob + ? eval/tofu_metrics/././../../collator@eval.tofu.metrics.model_utility.pre_compute.ra_Q_A_Prob_normalised.pre_compute.ra_Q_A_Prob.collators + : DataCollatorForSupervisedDatasetwithIndex + ? eval/tofu_metrics/././../../data/datasets@eval.tofu.metrics.model_utility.pre_compute.ra_Q_A_Prob_normalised.pre_compute.ra_Q_A_Prob.datasets + : TOFU_QA_ra + eval/tofu_metrics/.@eval.tofu.metrics.model_utility.pre_compute.retain_Truth_Ratio: retain_Truth_Ratio + eval/tofu_metrics/./.@eval.tofu.metrics.model_utility.pre_compute.retain_Truth_Ratio.pre_compute.retain_Q_A_PERT_Prob: retain_Q_A_PERT_Prob + ? eval/tofu_metrics/././../../collator@eval.tofu.metrics.model_utility.pre_compute.retain_Truth_Ratio.pre_compute.retain_Q_A_PERT_Prob.collators + : DataCollatorForSupervisedDatasetwithIndex + ? eval/tofu_metrics/././../../data/datasets@eval.tofu.metrics.model_utility.pre_compute.retain_Truth_Ratio.pre_compute.retain_Q_A_PERT_Prob.datasets + : TOFU_QA_retain_pert + eval/tofu_metrics/./.@eval.tofu.metrics.model_utility.pre_compute.retain_Truth_Ratio.pre_compute.retain_Q_A_PARA_Prob: retain_Q_A_PARA_Prob + ? eval/tofu_metrics/././../../collator@eval.tofu.metrics.model_utility.pre_compute.retain_Truth_Ratio.pre_compute.retain_Q_A_PARA_Prob.collators + : DataCollatorForSupervisedDatasetwithIndex + ? eval/tofu_metrics/././../../data/datasets@eval.tofu.metrics.model_utility.pre_compute.retain_Truth_Ratio.pre_compute.retain_Q_A_PARA_Prob.datasets + : TOFU_QA_retain_para + eval/tofu_metrics/.@eval.tofu.metrics.model_utility.pre_compute.retain_Q_A_ROUGE: retain_Q_A_ROUGE + eval/tofu_metrics/./../../generation@eval.tofu.metrics.model_utility.pre_compute.retain_Q_A_ROUGE.generation_args: default + eval/tofu_metrics/./../../collator@eval.tofu.metrics.model_utility.pre_compute.retain_Q_A_ROUGE.collators: DataCollatorForSupervisedDatasetwithIndex + eval/tofu_metrics/./../../data/datasets@eval.tofu.metrics.model_utility.pre_compute.retain_Q_A_ROUGE.datasets: TOFU_QA_retain_eval + eval/tofu_metrics/.@eval.tofu.metrics.model_utility.pre_compute.retain_Q_A_Prob: retain_Q_A_Prob + eval/tofu_metrics/./../../collator@eval.tofu.metrics.model_utility.pre_compute.retain_Q_A_Prob.collators: DataCollatorForSupervisedDatasetwithIndex + eval/tofu_metrics/./../../data/datasets@eval.tofu.metrics.model_utility.pre_compute.retain_Q_A_Prob.datasets: TOFU_QA_retain_eval + eval/tofu_metrics/../../generation@eval.tofu.metrics.forget_Q_A_ROUGE.generation_args: default + eval/tofu_metrics/../../collator@eval.tofu.metrics.forget_Q_A_ROUGE.collators: DataCollatorForSupervisedDatasetwithIndex + eval/tofu_metrics/../../data/datasets@eval.tofu.metrics.forget_Q_A_ROUGE.datasets: TOFU_QA_forget + eval/tofu_metrics/../../collator@eval.tofu.metrics.forget_Q_A_Prob.collators: DataCollatorForSupervisedDatasetwithIndex + eval/tofu_metrics/../../data/datasets@eval.tofu.metrics.forget_Q_A_Prob.datasets: TOFU_QA_forget + eval/tofu_metrics/.@eval.tofu.metrics.forget_quality.pre_compute.forget_truth_ratio: forget_Truth_Ratio + eval/tofu_metrics/./.@eval.tofu.metrics.forget_quality.pre_compute.forget_truth_ratio.pre_compute.forget_Q_A_PERT_Prob: forget_Q_A_PERT_Prob + ? eval/tofu_metrics/././../../collator@eval.tofu.metrics.forget_quality.pre_compute.forget_truth_ratio.pre_compute.forget_Q_A_PERT_Prob.collators + : DataCollatorForSupervisedDatasetwithIndex + ? eval/tofu_metrics/././../../data/datasets@eval.tofu.metrics.forget_quality.pre_compute.forget_truth_ratio.pre_compute.forget_Q_A_PERT_Prob.datasets + : TOFU_QA_forget_pert + eval/tofu_metrics/./.@eval.tofu.metrics.forget_quality.pre_compute.forget_truth_ratio.pre_compute.forget_Q_A_PARA_Prob: forget_Q_A_PARA_Prob + ? eval/tofu_metrics/././../../collator@eval.tofu.metrics.forget_quality.pre_compute.forget_truth_ratio.pre_compute.forget_Q_A_PARA_Prob.collators + : DataCollatorForSupervisedDatasetwithIndex + ? eval/tofu_metrics/././../../data/datasets@eval.tofu.metrics.forget_quality.pre_compute.forget_truth_ratio.pre_compute.forget_Q_A_PARA_Prob.datasets + : TOFU_QA_forget_para + eval/tofu_metrics/.@eval.tofu.metrics.forget_truth_ratio.pre_compute.forget_Q_A_PERT_Prob: forget_Q_A_PERT_Prob + eval/tofu_metrics/./../../collator@eval.tofu.metrics.forget_truth_ratio.pre_compute.forget_Q_A_PERT_Prob.collators: DataCollatorForSupervisedDatasetwithIndex + eval/tofu_metrics/./../../data/datasets@eval.tofu.metrics.forget_truth_ratio.pre_compute.forget_Q_A_PERT_Prob.datasets: TOFU_QA_forget_pert + eval/tofu_metrics/.@eval.tofu.metrics.forget_truth_ratio.pre_compute.forget_Q_A_PARA_Prob: forget_Q_A_PARA_Prob + eval/tofu_metrics/./../../collator@eval.tofu.metrics.forget_truth_ratio.pre_compute.forget_Q_A_PARA_Prob.collators: DataCollatorForSupervisedDatasetwithIndex + eval/tofu_metrics/./../../data/datasets@eval.tofu.metrics.forget_truth_ratio.pre_compute.forget_Q_A_PARA_Prob.datasets: TOFU_QA_forget_para + collator: DataCollatorForSupervisedDataset + data: unlearn + data/datasets@data.eval: null + data/datasets@data.retain: TOFU_QA_retain + data/datasets@data.forget: TOFU_QA_forget + trainer: RMU + model: Llama-3.2-1B-Instruct + hydra/env: default + hydra/callbacks: null + hydra/job_logging: colorlog + hydra/hydra_logging: colorlog + hydra/hydra_help: default + hydra/help: default + hydra/sweeper: basic + hydra/launcher: basic + hydra/output: default + verbose: false diff --git a/.hydra/overrides.yaml b/.hydra/overrides.yaml new file mode 100644 index 0000000..8624054 --- /dev/null +++ b/.hydra/overrides.yaml @@ -0,0 +1,21 @@ +- experiment=unlearn/tofu/default.yaml +- trainer=RMU +- task_name=tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 +- model=Llama-3.2-1B-Instruct +- forget_split=forget10 +- retain_split=retain90 +- holdout_split=holdout10 +- model.model_args.pretrained_model_name_or_path=open-unlearning/tofu_Llama-3.2-1B-Instruct_full +- model.model_args.attn_implementation=sdpa +- trainer.args.per_device_train_batch_size=4 +- trainer.args.gradient_accumulation_steps=4 +- trainer.args.ddp_find_unused_parameters=true +- trainer.args.do_eval=false +- trainer.args.save_strategy=no +- +trainer.args.push_to_hub=true +- +trainer.args.hub_model_id=Jeesup/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 +- +trainer.args.hub_strategy=end +- +trainer.args.hub_private_repo=false +- paths.output_dir=saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 +- +quant=qat_w4 +- trainer.method_args.module_regex=model\.layers\.3 diff --git a/README.md b/README.md new file mode 100644 index 0000000..07812d1 --- /dev/null +++ b/README.md @@ -0,0 +1,56 @@ +--- +library_name: transformers +license: bsd-3-clause +base_model: open-unlearning/tofu_Llama-3.2-1B-Instruct_full +tags: +- generated_from_trainer +model-index: +- name: tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 + results: [] +--- + + + +# tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 + +This model is a fine-tuned version of [open-unlearning/tofu_Llama-3.2-1B-Instruct_full](https://huggingface.co/open-unlearning/tofu_Llama-3.2-1B-Instruct_full) on an unknown dataset. + +## Model description + +More information needed + +## Intended uses & limitations + +More information needed + +## Training and evaluation data + +More information needed + +## Training procedure + +### Training hyperparameters + +The following hyperparameters were used during training: +- learning_rate: 1e-05 +- train_batch_size: 4 +- eval_batch_size: 16 +- seed: 0 +- gradient_accumulation_steps: 4 +- total_train_batch_size: 16 +- optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments +- lr_scheduler_type: linear +- lr_scheduler_warmup_steps: 25 +- num_epochs: 10 + +### Training results + + + +### Framework versions + +- Transformers 4.51.3 +- Pytorch 2.11.0+cu128 +- Datasets 3.0.1 +- Tokenizers 0.21.4 diff --git a/RMU.log b/RMU.log new file mode 100644 index 0000000..f975015 --- /dev/null +++ b/RMU.log @@ -0,0 +1,465 @@ +[2026-05-21 11:57:33,698][model][INFO] - Setting pad_token as eos token: <|eot_id|> +[2026-05-21 11:57:37,813][evaluator][INFO] - Evaluations stored in the experiment directory: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 +[2026-05-21 11:57:39,435][trainer][INFO] - RMU Trainer loaded, output_dir: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4 +[2026-05-21 11:57:40,023][evaluator][INFO] - ***** Running TOFU evaluation suite ***** +[2026-05-21 11:57:40,023][evaluator][INFO] - Fine-grained evaluations will be saved to: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-0/evals/TOFU_EVAL.json +[2026-05-21 11:57:40,023][evaluator][INFO] - Aggregated evaluations will be summarised in: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-0/evals/TOFU_SUMMARY.json +[2026-05-21 11:57:41,506][metrics][INFO] - Evaluating forget_Q_A_PARA_Prob +[2026-05-21 11:57:58,264][metrics][INFO] - Evaluating forget_Q_A_PERT_Prob +[2026-05-21 11:58:38,873][metrics][INFO] - Evaluating forget_truth_ratio +[2026-05-21 11:58:38,874][evaluator][INFO] - Result for metric forget_truth_ratio: 0.6448834990772232 +[2026-05-21 11:58:38,880][metrics][INFO] - Skipping forget_quality's precompute forget_truth_ratio, already evaluated. +[2026-05-21 11:58:38,881][metrics][INFO] - Evaluating forget_quality +[2026-05-21 11:58:38,881][metrics][WARNING] - retain_model_logs not provided in reference_logs, setting forget_quality to None +[2026-05-21 11:58:38,881][evaluator][INFO] - Result for metric forget_quality: None +[2026-05-21 11:58:40,563][metrics][INFO] - Evaluating forget_Q_A_Prob +[2026-05-21 11:58:46,744][evaluator][INFO] - Result for metric forget_Q_A_Prob: 0.10887884757248685 +[2026-05-21 11:58:48,505][metrics][INFO] - Evaluating forget_Q_A_ROUGE +[2026-05-21 12:02:17,635][evaluator][INFO] - Result for metric forget_Q_A_ROUGE: 0.27890823225307393 +[2026-05-21 12:02:19,330][metrics][INFO] - Evaluating retain_Q_A_Prob +[2026-05-21 12:02:27,753][metrics][INFO] - Evaluating retain_Q_A_ROUGE +[2026-05-21 12:04:33,587][metrics][INFO] - Evaluating retain_Q_A_PARA_Prob +[2026-05-21 12:04:41,251][metrics][INFO] - Evaluating retain_Q_A_PERT_Prob +[2026-05-21 12:05:12,905][metrics][INFO] - Evaluating retain_Truth_Ratio +[2026-05-21 12:05:14,652][metrics][INFO] - Evaluating ra_Q_A_Prob +[2026-05-21 12:05:18,181][metrics][INFO] - Evaluating ra_Q_A_PERT_Prob +[2026-05-21 12:05:21,757][metrics][INFO] - Evaluating ra_Q_A_Prob_normalised +[2026-05-21 12:05:23,000][metrics][INFO] - Evaluating ra_Q_A_ROUGE +[2026-05-21 12:06:07,138][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_Prob, already evaluated. +[2026-05-21 12:06:07,139][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_PERT_Prob, already evaluated. +[2026-05-21 12:06:07,139][metrics][INFO] - Evaluating ra_Truth_Ratio +[2026-05-21 12:06:08,822][metrics][INFO] - Evaluating wf_Q_A_Prob +[2026-05-21 12:06:11,152][metrics][INFO] - Evaluating wf_Q_A_PERT_Prob +[2026-05-21 12:06:15,810][metrics][INFO] - Evaluating wf_Q_A_Prob_normalised +[2026-05-21 12:06:17,088][metrics][INFO] - Evaluating wf_Q_A_ROUGE +[2026-05-21 12:07:14,539][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_Prob, already evaluated. +[2026-05-21 12:07:14,539][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_PERT_Prob, already evaluated. +[2026-05-21 12:07:14,539][metrics][INFO] - Evaluating wf_Truth_Ratio +[2026-05-21 12:07:14,540][metrics][INFO] - Evaluating model_utility +[2026-05-21 12:07:14,544][evaluator][INFO] - Result for metric model_utility: 0.2185219733034594 +[2026-05-21 12:07:17,574][metrics][INFO] - Evaluating mia_min_k +[2026-05-21 12:07:21,214][metrics][INFO] - Evaluating privleak +[2026-05-21 12:07:21,214][metrics][WARNING] - retain_model_logs evals not provided for privleak, using default retain auc of 0.5 +[2026-05-21 12:07:21,214][evaluator][INFO] - Result for metric privleak: -47.96999999040602 +[2026-05-21 12:07:22,668][metrics][INFO] - Evaluating extraction_strength +[2026-05-21 12:07:23,965][evaluator][INFO] - Result for metric extraction_strength: 0.05119585323242784 +[2026-05-21 12:08:53,751][evaluator][INFO] - ***** Running TOFU evaluation suite ***** +[2026-05-21 12:08:53,751][evaluator][INFO] - Fine-grained evaluations will be saved to: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-25/evals/TOFU_EVAL.json +[2026-05-21 12:08:53,751][evaluator][INFO] - Aggregated evaluations will be summarised in: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-25/evals/TOFU_SUMMARY.json +[2026-05-21 12:08:55,728][metrics][INFO] - Evaluating forget_Q_A_PARA_Prob +[2026-05-21 12:09:02,060][metrics][INFO] - Evaluating forget_Q_A_PERT_Prob +[2026-05-21 12:09:26,356][metrics][INFO] - Evaluating forget_truth_ratio +[2026-05-21 12:09:26,357][evaluator][INFO] - Result for metric forget_truth_ratio: 0.6614321776157142 +[2026-05-21 12:09:26,364][metrics][INFO] - Skipping forget_quality's precompute forget_truth_ratio, already evaluated. +[2026-05-21 12:09:26,364][metrics][INFO] - Evaluating forget_quality +[2026-05-21 12:09:26,364][metrics][WARNING] - retain_model_logs not provided in reference_logs, setting forget_quality to None +[2026-05-21 12:09:26,364][evaluator][INFO] - Result for metric forget_quality: None +[2026-05-21 12:09:28,002][metrics][INFO] - Evaluating forget_Q_A_Prob +[2026-05-21 12:09:33,162][evaluator][INFO] - Result for metric forget_Q_A_Prob: 0.044040033797500655 +[2026-05-21 12:09:34,852][metrics][INFO] - Evaluating forget_Q_A_ROUGE +[2026-05-21 12:11:29,404][evaluator][INFO] - Result for metric forget_Q_A_ROUGE: 0.19825707891118127 +[2026-05-21 12:11:31,096][metrics][INFO] - Evaluating retain_Q_A_Prob +[2026-05-21 12:11:39,019][metrics][INFO] - Evaluating retain_Q_A_ROUGE +[2026-05-21 12:13:45,155][metrics][INFO] - Evaluating retain_Q_A_PARA_Prob +[2026-05-21 12:13:52,720][metrics][INFO] - Evaluating retain_Q_A_PERT_Prob +[2026-05-21 12:14:21,986][metrics][INFO] - Evaluating retain_Truth_Ratio +[2026-05-21 12:14:23,693][metrics][INFO] - Evaluating ra_Q_A_Prob +[2026-05-21 12:14:26,349][metrics][INFO] - Evaluating ra_Q_A_PERT_Prob +[2026-05-21 12:14:30,389][metrics][INFO] - Evaluating ra_Q_A_Prob_normalised +[2026-05-21 12:14:31,749][metrics][INFO] - Evaluating ra_Q_A_ROUGE +[2026-05-21 12:15:44,420][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_Prob, already evaluated. +[2026-05-21 12:15:44,421][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_PERT_Prob, already evaluated. +[2026-05-21 12:15:44,421][metrics][INFO] - Evaluating ra_Truth_Ratio +[2026-05-21 12:15:46,154][metrics][INFO] - Evaluating wf_Q_A_Prob +[2026-05-21 12:15:48,537][metrics][INFO] - Evaluating wf_Q_A_PERT_Prob +[2026-05-21 12:15:51,980][metrics][INFO] - Evaluating wf_Q_A_Prob_normalised +[2026-05-21 12:15:53,337][metrics][INFO] - Evaluating wf_Q_A_ROUGE +[2026-05-21 12:16:22,460][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_Prob, already evaluated. +[2026-05-21 12:16:22,460][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_PERT_Prob, already evaluated. +[2026-05-21 12:16:22,460][metrics][INFO] - Evaluating wf_Truth_Ratio +[2026-05-21 12:16:22,461][metrics][INFO] - Evaluating model_utility +[2026-05-21 12:16:22,461][evaluator][INFO] - Result for metric model_utility: 0.08605862105946929 +[2026-05-21 12:16:25,761][metrics][INFO] - Evaluating mia_min_k +[2026-05-21 12:16:31,351][metrics][INFO] - Evaluating privleak +[2026-05-21 12:16:31,351][metrics][WARNING] - retain_model_logs evals not provided for privleak, using default retain auc of 0.5 +[2026-05-21 12:16:31,351][evaluator][INFO] - Result for metric privleak: -22.12749999557451 +[2026-05-21 12:16:33,075][metrics][INFO] - Evaluating extraction_strength +[2026-05-21 12:16:42,339][evaluator][INFO] - Result for metric extraction_strength: 0.04014118794649353 +[2026-05-21 12:17:06,543][evaluator][INFO] - ***** Running TOFU evaluation suite ***** +[2026-05-21 12:17:06,543][evaluator][INFO] - Fine-grained evaluations will be saved to: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-50/evals/TOFU_EVAL.json +[2026-05-21 12:17:06,544][evaluator][INFO] - Aggregated evaluations will be summarised in: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-50/evals/TOFU_SUMMARY.json +[2026-05-21 12:17:08,209][metrics][INFO] - Evaluating forget_Q_A_PARA_Prob +[2026-05-21 12:17:16,386][metrics][INFO] - Evaluating forget_Q_A_PERT_Prob +[2026-05-21 12:17:44,453][metrics][INFO] - Evaluating forget_truth_ratio +[2026-05-21 12:17:44,454][evaluator][INFO] - Result for metric forget_truth_ratio: 0.6646114327873448 +[2026-05-21 12:17:44,460][metrics][INFO] - Skipping forget_quality's precompute forget_truth_ratio, already evaluated. +[2026-05-21 12:17:44,461][metrics][INFO] - Evaluating forget_quality +[2026-05-21 12:17:44,461][metrics][WARNING] - retain_model_logs not provided in reference_logs, setting forget_quality to None +[2026-05-21 12:17:44,461][evaluator][INFO] - Result for metric forget_quality: None +[2026-05-21 12:17:46,123][metrics][INFO] - Evaluating forget_Q_A_Prob +[2026-05-21 12:17:51,886][evaluator][INFO] - Result for metric forget_Q_A_Prob: 0.028171376134705498 +[2026-05-21 12:17:53,595][metrics][INFO] - Evaluating forget_Q_A_ROUGE +[2026-05-21 12:19:56,169][evaluator][INFO] - Result for metric forget_Q_A_ROUGE: 0.18136216120754584 +[2026-05-21 12:19:58,075][metrics][INFO] - Evaluating retain_Q_A_Prob +[2026-05-21 12:20:05,461][metrics][INFO] - Evaluating retain_Q_A_ROUGE +[2026-05-21 12:22:11,777][metrics][INFO] - Evaluating retain_Q_A_PARA_Prob +[2026-05-21 12:22:19,434][metrics][INFO] - Evaluating retain_Q_A_PERT_Prob +[2026-05-21 12:22:48,615][metrics][INFO] - Evaluating retain_Truth_Ratio +[2026-05-21 12:22:50,385][metrics][INFO] - Evaluating ra_Q_A_Prob +[2026-05-21 12:22:53,191][metrics][INFO] - Evaluating ra_Q_A_PERT_Prob +[2026-05-21 12:22:57,341][metrics][INFO] - Evaluating ra_Q_A_Prob_normalised +[2026-05-21 12:22:58,995][metrics][INFO] - Evaluating ra_Q_A_ROUGE +[2026-05-21 12:23:38,005][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_Prob, already evaluated. +[2026-05-21 12:23:38,005][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_PERT_Prob, already evaluated. +[2026-05-21 12:23:38,005][metrics][INFO] - Evaluating ra_Truth_Ratio +[2026-05-21 12:23:39,646][metrics][INFO] - Evaluating wf_Q_A_Prob +[2026-05-21 12:23:42,040][metrics][INFO] - Evaluating wf_Q_A_PERT_Prob +[2026-05-21 12:23:45,003][metrics][INFO] - Evaluating wf_Q_A_Prob_normalised +[2026-05-21 12:23:46,257][metrics][INFO] - Evaluating wf_Q_A_ROUGE +[2026-05-21 12:24:23,368][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_Prob, already evaluated. +[2026-05-21 12:24:23,369][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_PERT_Prob, already evaluated. +[2026-05-21 12:24:23,369][metrics][INFO] - Evaluating wf_Truth_Ratio +[2026-05-21 12:24:23,369][metrics][INFO] - Evaluating model_utility +[2026-05-21 12:24:23,370][evaluator][INFO] - Result for metric model_utility: 0.07766678967490327 +[2026-05-21 12:24:26,397][metrics][INFO] - Evaluating mia_min_k +[2026-05-21 12:24:31,922][metrics][INFO] - Evaluating privleak +[2026-05-21 12:24:31,923][metrics][WARNING] - retain_model_logs evals not provided for privleak, using default retain auc of 0.5 +[2026-05-21 12:24:31,923][evaluator][INFO] - Result for metric privleak: -16.837499996632495 +[2026-05-21 12:24:33,630][metrics][INFO] - Evaluating extraction_strength +[2026-05-21 12:24:42,580][evaluator][INFO] - Result for metric extraction_strength: 0.03555271291935598 +[2026-05-21 12:25:09,643][evaluator][INFO] - ***** Running TOFU evaluation suite ***** +[2026-05-21 12:25:09,643][evaluator][INFO] - Fine-grained evaluations will be saved to: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-75/evals/TOFU_EVAL.json +[2026-05-21 12:25:09,643][evaluator][INFO] - Aggregated evaluations will be summarised in: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-75/evals/TOFU_SUMMARY.json +[2026-05-21 12:25:11,296][metrics][INFO] - Evaluating forget_Q_A_PARA_Prob +[2026-05-21 12:25:19,068][metrics][INFO] - Evaluating forget_Q_A_PERT_Prob +[2026-05-21 12:25:36,970][metrics][INFO] - Evaluating forget_truth_ratio +[2026-05-21 12:25:36,971][evaluator][INFO] - Result for metric forget_truth_ratio: 0.666956633608444 +[2026-05-21 12:25:36,977][metrics][INFO] - Skipping forget_quality's precompute forget_truth_ratio, already evaluated. +[2026-05-21 12:25:36,978][metrics][INFO] - Evaluating forget_quality +[2026-05-21 12:25:36,978][metrics][WARNING] - retain_model_logs not provided in reference_logs, setting forget_quality to None +[2026-05-21 12:25:36,978][evaluator][INFO] - Result for metric forget_quality: None +[2026-05-21 12:25:38,613][metrics][INFO] - Evaluating forget_Q_A_Prob +[2026-05-21 12:25:44,827][evaluator][INFO] - Result for metric forget_Q_A_Prob: 0.025599835189350415 +[2026-05-21 12:25:46,467][metrics][INFO] - Evaluating forget_Q_A_ROUGE +[2026-05-21 12:27:48,567][evaluator][INFO] - Result for metric forget_Q_A_ROUGE: 0.17665323136017774 +[2026-05-21 12:27:50,319][metrics][INFO] - Evaluating retain_Q_A_Prob +[2026-05-21 12:27:58,030][metrics][INFO] - Evaluating retain_Q_A_ROUGE +[2026-05-21 12:30:05,340][metrics][INFO] - Evaluating retain_Q_A_PARA_Prob +[2026-05-21 12:30:12,963][metrics][INFO] - Evaluating retain_Q_A_PERT_Prob +[2026-05-21 12:30:39,143][metrics][INFO] - Evaluating retain_Truth_Ratio +[2026-05-21 12:30:40,835][metrics][INFO] - Evaluating ra_Q_A_Prob +[2026-05-21 12:30:43,488][metrics][INFO] - Evaluating ra_Q_A_PERT_Prob +[2026-05-21 12:30:47,643][metrics][INFO] - Evaluating ra_Q_A_Prob_normalised +[2026-05-21 12:30:48,950][metrics][INFO] - Evaluating ra_Q_A_ROUGE +[2026-05-21 12:31:28,106][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_Prob, already evaluated. +[2026-05-21 12:31:28,107][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_PERT_Prob, already evaluated. +[2026-05-21 12:31:28,107][metrics][INFO] - Evaluating ra_Truth_Ratio +[2026-05-21 12:31:29,859][metrics][INFO] - Evaluating wf_Q_A_Prob +[2026-05-21 12:31:32,220][metrics][INFO] - Evaluating wf_Q_A_PERT_Prob +[2026-05-21 12:31:35,651][metrics][INFO] - Evaluating wf_Q_A_Prob_normalised +[2026-05-21 12:31:36,902][metrics][INFO] - Evaluating wf_Q_A_ROUGE +[2026-05-21 12:32:16,255][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_Prob, already evaluated. +[2026-05-21 12:32:16,256][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_PERT_Prob, already evaluated. +[2026-05-21 12:32:16,256][metrics][INFO] - Evaluating wf_Truth_Ratio +[2026-05-21 12:32:16,256][metrics][INFO] - Evaluating model_utility +[2026-05-21 12:32:16,257][evaluator][INFO] - Result for metric model_utility: 0.025169849004668194 +[2026-05-21 12:32:19,201][metrics][INFO] - Evaluating mia_min_k +[2026-05-21 12:32:24,841][metrics][INFO] - Evaluating privleak +[2026-05-21 12:32:24,841][metrics][WARNING] - retain_model_logs evals not provided for privleak, using default retain auc of 0.5 +[2026-05-21 12:32:24,841][evaluator][INFO] - Result for metric privleak: -15.60999999687799 +[2026-05-21 12:32:26,598][metrics][INFO] - Evaluating extraction_strength +[2026-05-21 12:32:30,837][evaluator][INFO] - Result for metric extraction_strength: 0.03542379317218397 +[2026-05-21 12:32:55,301][evaluator][INFO] - ***** Running TOFU evaluation suite ***** +[2026-05-21 12:32:55,301][evaluator][INFO] - Fine-grained evaluations will be saved to: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-100/evals/TOFU_EVAL.json +[2026-05-21 12:32:55,301][evaluator][INFO] - Aggregated evaluations will be summarised in: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-100/evals/TOFU_SUMMARY.json +[2026-05-21 12:32:56,927][metrics][INFO] - Evaluating forget_Q_A_PARA_Prob +[2026-05-21 12:33:02,940][metrics][INFO] - Evaluating forget_Q_A_PERT_Prob +[2026-05-21 12:33:32,151][metrics][INFO] - Evaluating forget_truth_ratio +[2026-05-21 12:33:32,152][evaluator][INFO] - Result for metric forget_truth_ratio: 0.6650891487506957 +[2026-05-21 12:33:32,158][metrics][INFO] - Skipping forget_quality's precompute forget_truth_ratio, already evaluated. +[2026-05-21 12:33:32,159][metrics][INFO] - Evaluating forget_quality +[2026-05-21 12:33:32,159][metrics][WARNING] - retain_model_logs not provided in reference_logs, setting forget_quality to None +[2026-05-21 12:33:32,159][evaluator][INFO] - Result for metric forget_quality: None +[2026-05-21 12:33:33,843][metrics][INFO] - Evaluating forget_Q_A_Prob +[2026-05-21 12:33:38,385][evaluator][INFO] - Result for metric forget_Q_A_Prob: 0.024368888099124887 +[2026-05-21 12:33:39,662][metrics][INFO] - Evaluating forget_Q_A_ROUGE +[2026-05-21 12:35:08,076][evaluator][INFO] - Result for metric forget_Q_A_ROUGE: 0.17340090189465512 +[2026-05-21 12:35:09,710][metrics][INFO] - Evaluating retain_Q_A_Prob +[2026-05-21 12:35:14,668][metrics][INFO] - Evaluating retain_Q_A_ROUGE +[2026-05-21 12:37:19,876][metrics][INFO] - Evaluating retain_Q_A_PARA_Prob +[2026-05-21 12:37:27,741][metrics][INFO] - Evaluating retain_Q_A_PERT_Prob +[2026-05-21 12:37:55,967][metrics][INFO] - Evaluating retain_Truth_Ratio +[2026-05-21 12:37:57,746][metrics][INFO] - Evaluating ra_Q_A_Prob +[2026-05-21 12:38:00,481][metrics][INFO] - Evaluating ra_Q_A_PERT_Prob +[2026-05-21 12:38:04,623][metrics][INFO] - Evaluating ra_Q_A_Prob_normalised +[2026-05-21 12:38:06,064][metrics][INFO] - Evaluating ra_Q_A_ROUGE +[2026-05-21 12:38:40,084][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_Prob, already evaluated. +[2026-05-21 12:38:40,084][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_PERT_Prob, already evaluated. +[2026-05-21 12:38:40,084][metrics][INFO] - Evaluating ra_Truth_Ratio +[2026-05-21 12:38:41,711][metrics][INFO] - Evaluating wf_Q_A_Prob +[2026-05-21 12:38:44,062][metrics][INFO] - Evaluating wf_Q_A_PERT_Prob +[2026-05-21 12:38:47,631][metrics][INFO] - Evaluating wf_Q_A_Prob_normalised +[2026-05-21 12:38:48,878][metrics][INFO] - Evaluating wf_Q_A_ROUGE +[2026-05-21 12:39:28,314][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_Prob, already evaluated. +[2026-05-21 12:39:28,314][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_PERT_Prob, already evaluated. +[2026-05-21 12:39:28,314][metrics][INFO] - Evaluating wf_Truth_Ratio +[2026-05-21 12:39:28,315][metrics][INFO] - Evaluating model_utility +[2026-05-21 12:39:28,315][evaluator][INFO] - Result for metric model_utility: 0.07572111776600261 +[2026-05-21 12:39:31,238][metrics][INFO] - Evaluating mia_min_k +[2026-05-21 12:39:36,722][metrics][INFO] - Evaluating privleak +[2026-05-21 12:39:36,722][metrics][WARNING] - retain_model_logs evals not provided for privleak, using default retain auc of 0.5 +[2026-05-21 12:39:36,722][evaluator][INFO] - Result for metric privleak: -15.054999996988997 +[2026-05-21 12:39:38,473][metrics][INFO] - Evaluating extraction_strength +[2026-05-21 12:39:45,286][evaluator][INFO] - Result for metric extraction_strength: 0.034881082155764 +[2026-05-21 12:40:09,613][evaluator][INFO] - ***** Running TOFU evaluation suite ***** +[2026-05-21 12:40:09,613][evaluator][INFO] - Fine-grained evaluations will be saved to: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-125/evals/TOFU_EVAL.json +[2026-05-21 12:40:09,613][evaluator][INFO] - Aggregated evaluations will be summarised in: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-125/evals/TOFU_SUMMARY.json +[2026-05-21 12:40:11,550][metrics][INFO] - Evaluating forget_Q_A_PARA_Prob +[2026-05-21 12:40:19,424][metrics][INFO] - Evaluating forget_Q_A_PERT_Prob +[2026-05-21 12:40:49,178][metrics][INFO] - Evaluating forget_truth_ratio +[2026-05-21 12:40:49,179][evaluator][INFO] - Result for metric forget_truth_ratio: 0.6653264816436281 +[2026-05-21 12:40:49,185][metrics][INFO] - Skipping forget_quality's precompute forget_truth_ratio, already evaluated. +[2026-05-21 12:40:49,186][metrics][INFO] - Evaluating forget_quality +[2026-05-21 12:40:49,186][metrics][WARNING] - retain_model_logs not provided in reference_logs, setting forget_quality to None +[2026-05-21 12:40:49,186][evaluator][INFO] - Result for metric forget_quality: None +[2026-05-21 12:40:50,857][metrics][INFO] - Evaluating forget_Q_A_Prob +[2026-05-21 12:40:56,868][evaluator][INFO] - Result for metric forget_Q_A_Prob: 0.023090821445148322 +[2026-05-21 12:40:58,563][metrics][INFO] - Evaluating forget_Q_A_ROUGE +[2026-05-21 12:42:59,770][evaluator][INFO] - Result for metric forget_Q_A_ROUGE: 0.17290647721627245 +[2026-05-21 12:43:01,413][metrics][INFO] - Evaluating retain_Q_A_Prob +[2026-05-21 12:43:08,531][metrics][INFO] - Evaluating retain_Q_A_ROUGE +[2026-05-21 12:44:48,920][metrics][INFO] - Evaluating retain_Q_A_PARA_Prob +[2026-05-21 12:44:56,881][metrics][INFO] - Evaluating retain_Q_A_PERT_Prob +[2026-05-21 12:45:26,437][metrics][INFO] - Evaluating retain_Truth_Ratio +[2026-05-21 12:45:28,234][metrics][INFO] - Evaluating ra_Q_A_Prob +[2026-05-21 12:45:31,003][metrics][INFO] - Evaluating ra_Q_A_PERT_Prob +[2026-05-21 12:45:35,107][metrics][INFO] - Evaluating ra_Q_A_Prob_normalised +[2026-05-21 12:45:36,368][metrics][INFO] - Evaluating ra_Q_A_ROUGE +[2026-05-21 12:46:14,962][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_Prob, already evaluated. +[2026-05-21 12:46:14,962][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_PERT_Prob, already evaluated. +[2026-05-21 12:46:14,962][metrics][INFO] - Evaluating ra_Truth_Ratio +[2026-05-21 12:46:16,815][metrics][INFO] - Evaluating wf_Q_A_Prob +[2026-05-21 12:46:19,204][metrics][INFO] - Evaluating wf_Q_A_PERT_Prob +[2026-05-21 12:46:21,563][metrics][INFO] - Evaluating wf_Q_A_Prob_normalised +[2026-05-21 12:46:22,945][metrics][INFO] - Evaluating wf_Q_A_ROUGE +[2026-05-21 12:46:59,910][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_Prob, already evaluated. +[2026-05-21 12:46:59,911][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_PERT_Prob, already evaluated. +[2026-05-21 12:46:59,911][metrics][INFO] - Evaluating wf_Truth_Ratio +[2026-05-21 12:46:59,911][metrics][INFO] - Evaluating model_utility +[2026-05-21 12:46:59,912][evaluator][INFO] - Result for metric model_utility: 0.061282254233379956 +[2026-05-21 12:47:02,933][metrics][INFO] - Evaluating mia_min_k +[2026-05-21 12:47:08,271][metrics][INFO] - Evaluating privleak +[2026-05-21 12:47:08,271][metrics][WARNING] - retain_model_logs evals not provided for privleak, using default retain auc of 0.5 +[2026-05-21 12:47:08,271][evaluator][INFO] - Result for metric privleak: -14.518749997096252 +[2026-05-21 12:47:10,069][metrics][INFO] - Evaluating extraction_strength +[2026-05-21 12:47:32,061][evaluator][INFO] - Result for metric extraction_strength: 0.03492969775585019 +[2026-05-21 12:47:52,836][evaluator][INFO] - ***** Running TOFU evaluation suite ***** +[2026-05-21 12:47:52,836][evaluator][INFO] - Fine-grained evaluations will be saved to: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-150/evals/TOFU_EVAL.json +[2026-05-21 12:47:52,836][evaluator][INFO] - Aggregated evaluations will be summarised in: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-150/evals/TOFU_SUMMARY.json +[2026-05-21 12:47:54,493][metrics][INFO] - Evaluating forget_Q_A_PARA_Prob +[2026-05-21 12:48:02,146][metrics][INFO] - Evaluating forget_Q_A_PERT_Prob +[2026-05-21 12:48:31,769][metrics][INFO] - Evaluating forget_truth_ratio +[2026-05-21 12:48:31,770][evaluator][INFO] - Result for metric forget_truth_ratio: 0.665408632731608 +[2026-05-21 12:48:31,776][metrics][INFO] - Skipping forget_quality's precompute forget_truth_ratio, already evaluated. +[2026-05-21 12:48:31,777][metrics][INFO] - Evaluating forget_quality +[2026-05-21 12:48:31,777][metrics][WARNING] - retain_model_logs not provided in reference_logs, setting forget_quality to None +[2026-05-21 12:48:31,777][evaluator][INFO] - Result for metric forget_quality: None +[2026-05-21 12:48:33,422][metrics][INFO] - Evaluating forget_Q_A_Prob +[2026-05-21 12:48:37,862][evaluator][INFO] - Result for metric forget_Q_A_Prob: 0.022637412079275235 +[2026-05-21 12:48:39,116][metrics][INFO] - Evaluating forget_Q_A_ROUGE +[2026-05-21 12:50:46,079][evaluator][INFO] - Result for metric forget_Q_A_ROUGE: 0.16969368902404242 +[2026-05-21 12:50:48,146][metrics][INFO] - Evaluating retain_Q_A_Prob +[2026-05-21 12:50:54,741][metrics][INFO] - Evaluating retain_Q_A_ROUGE +[2026-05-21 12:52:56,349][metrics][INFO] - Evaluating retain_Q_A_PARA_Prob +[2026-05-21 12:53:02,778][metrics][INFO] - Evaluating retain_Q_A_PERT_Prob +[2026-05-21 12:53:20,514][metrics][INFO] - Evaluating retain_Truth_Ratio +[2026-05-21 12:53:22,173][metrics][INFO] - Evaluating ra_Q_A_Prob +[2026-05-21 12:53:25,211][metrics][INFO] - Evaluating ra_Q_A_PERT_Prob +[2026-05-21 12:53:29,602][metrics][INFO] - Evaluating ra_Q_A_Prob_normalised +[2026-05-21 12:53:31,058][metrics][INFO] - Evaluating ra_Q_A_ROUGE +[2026-05-21 12:54:09,744][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_Prob, already evaluated. +[2026-05-21 12:54:09,745][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_PERT_Prob, already evaluated. +[2026-05-21 12:54:09,745][metrics][INFO] - Evaluating ra_Truth_Ratio +[2026-05-21 12:54:11,393][metrics][INFO] - Evaluating wf_Q_A_Prob +[2026-05-21 12:54:13,787][metrics][INFO] - Evaluating wf_Q_A_PERT_Prob +[2026-05-21 12:54:17,260][metrics][INFO] - Evaluating wf_Q_A_Prob_normalised +[2026-05-21 12:54:18,762][metrics][INFO] - Evaluating wf_Q_A_ROUGE +[2026-05-21 12:54:58,987][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_Prob, already evaluated. +[2026-05-21 12:54:58,988][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_PERT_Prob, already evaluated. +[2026-05-21 12:54:58,988][metrics][INFO] - Evaluating wf_Truth_Ratio +[2026-05-21 12:54:58,988][metrics][INFO] - Evaluating model_utility +[2026-05-21 12:54:58,989][evaluator][INFO] - Result for metric model_utility: 0.07293884758887383 +[2026-05-21 12:55:01,964][metrics][INFO] - Evaluating mia_min_k +[2026-05-21 12:55:04,458][metrics][INFO] - Evaluating privleak +[2026-05-21 12:55:04,458][metrics][WARNING] - retain_model_logs evals not provided for privleak, using default retain auc of 0.5 +[2026-05-21 12:55:04,458][evaluator][INFO] - Result for metric privleak: -14.298749997140254 +[2026-05-21 12:55:06,059][metrics][INFO] - Evaluating extraction_strength +[2026-05-21 12:55:09,527][evaluator][INFO] - Result for metric extraction_strength: 0.034710650136802565 +[2026-05-21 12:55:31,076][evaluator][INFO] - ***** Running TOFU evaluation suite ***** +[2026-05-21 12:55:31,076][evaluator][INFO] - Fine-grained evaluations will be saved to: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-175/evals/TOFU_EVAL.json +[2026-05-21 12:55:31,076][evaluator][INFO] - Aggregated evaluations will be summarised in: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-175/evals/TOFU_SUMMARY.json +[2026-05-21 12:55:32,704][metrics][INFO] - Evaluating forget_Q_A_PARA_Prob +[2026-05-21 12:55:38,755][metrics][INFO] - Evaluating forget_Q_A_PERT_Prob +[2026-05-21 12:56:06,854][metrics][INFO] - Evaluating forget_truth_ratio +[2026-05-21 12:56:06,855][evaluator][INFO] - Result for metric forget_truth_ratio: 0.6661571899518759 +[2026-05-21 12:56:06,861][metrics][INFO] - Skipping forget_quality's precompute forget_truth_ratio, already evaluated. +[2026-05-21 12:56:06,862][metrics][INFO] - Evaluating forget_quality +[2026-05-21 12:56:06,862][metrics][WARNING] - retain_model_logs not provided in reference_logs, setting forget_quality to None +[2026-05-21 12:56:06,862][evaluator][INFO] - Result for metric forget_quality: None +[2026-05-21 12:56:08,508][metrics][INFO] - Evaluating forget_Q_A_Prob +[2026-05-21 12:56:14,367][evaluator][INFO] - Result for metric forget_Q_A_Prob: 0.022689154595791478 +[2026-05-21 12:56:16,040][metrics][INFO] - Evaluating forget_Q_A_ROUGE +[2026-05-21 12:58:20,768][evaluator][INFO] - Result for metric forget_Q_A_ROUGE: 0.17071292253126477 +[2026-05-21 12:58:22,468][metrics][INFO] - Evaluating retain_Q_A_Prob +[2026-05-21 12:58:29,886][metrics][INFO] - Evaluating retain_Q_A_ROUGE +[2026-05-21 13:00:37,081][metrics][INFO] - Evaluating retain_Q_A_PARA_Prob +[2026-05-21 13:00:43,094][metrics][INFO] - Evaluating retain_Q_A_PERT_Prob +[2026-05-21 13:01:10,854][metrics][INFO] - Evaluating retain_Truth_Ratio +[2026-05-21 13:01:12,568][metrics][INFO] - Evaluating ra_Q_A_Prob +[2026-05-21 13:01:15,228][metrics][INFO] - Evaluating ra_Q_A_PERT_Prob +[2026-05-21 13:01:19,302][metrics][INFO] - Evaluating ra_Q_A_Prob_normalised +[2026-05-21 13:01:20,611][metrics][INFO] - Evaluating ra_Q_A_ROUGE +[2026-05-21 13:01:54,745][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_Prob, already evaluated. +[2026-05-21 13:01:54,745][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_PERT_Prob, already evaluated. +[2026-05-21 13:01:54,745][metrics][INFO] - Evaluating ra_Truth_Ratio +[2026-05-21 13:01:56,526][metrics][INFO] - Evaluating wf_Q_A_Prob +[2026-05-21 13:01:58,924][metrics][INFO] - Evaluating wf_Q_A_PERT_Prob +[2026-05-21 13:02:02,375][metrics][INFO] - Evaluating wf_Q_A_Prob_normalised +[2026-05-21 13:02:03,624][metrics][INFO] - Evaluating wf_Q_A_ROUGE +[2026-05-21 13:02:27,784][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_Prob, already evaluated. +[2026-05-21 13:02:27,784][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_PERT_Prob, already evaluated. +[2026-05-21 13:02:27,784][metrics][INFO] - Evaluating wf_Truth_Ratio +[2026-05-21 13:02:27,784][metrics][INFO] - Evaluating model_utility +[2026-05-21 13:02:27,785][evaluator][INFO] - Result for metric model_utility: 0.024938334488339972 +[2026-05-21 13:02:30,834][metrics][INFO] - Evaluating mia_min_k +[2026-05-21 13:02:35,356][metrics][INFO] - Evaluating privleak +[2026-05-21 13:02:35,356][metrics][WARNING] - retain_model_logs evals not provided for privleak, using default retain auc of 0.5 +[2026-05-21 13:02:35,356][evaluator][INFO] - Result for metric privleak: -14.50124999709975 +[2026-05-21 13:02:36,768][metrics][INFO] - Evaluating extraction_strength +[2026-05-21 13:02:52,494][evaluator][INFO] - Result for metric extraction_strength: 0.03454297434412678 +[2026-05-21 13:03:16,128][evaluator][INFO] - ***** Running TOFU evaluation suite ***** +[2026-05-21 13:03:16,129][evaluator][INFO] - Fine-grained evaluations will be saved to: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-200/evals/TOFU_EVAL.json +[2026-05-21 13:03:16,129][evaluator][INFO] - Aggregated evaluations will be summarised in: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-200/evals/TOFU_SUMMARY.json +[2026-05-21 13:03:17,801][metrics][INFO] - Evaluating forget_Q_A_PARA_Prob +[2026-05-21 13:03:25,631][metrics][INFO] - Evaluating forget_Q_A_PERT_Prob +[2026-05-21 13:03:56,057][metrics][INFO] - Evaluating forget_truth_ratio +[2026-05-21 13:03:56,058][evaluator][INFO] - Result for metric forget_truth_ratio: 0.6657672475603854 +[2026-05-21 13:03:56,064][metrics][INFO] - Skipping forget_quality's precompute forget_truth_ratio, already evaluated. +[2026-05-21 13:03:56,065][metrics][INFO] - Evaluating forget_quality +[2026-05-21 13:03:56,065][metrics][WARNING] - retain_model_logs not provided in reference_logs, setting forget_quality to None +[2026-05-21 13:03:56,065][evaluator][INFO] - Result for metric forget_quality: None +[2026-05-21 13:03:57,750][metrics][INFO] - Evaluating forget_Q_A_Prob +[2026-05-21 13:04:02,208][evaluator][INFO] - Result for metric forget_Q_A_Prob: 0.022478839480972967 +[2026-05-21 13:04:03,476][metrics][INFO] - Evaluating forget_Q_A_ROUGE +[2026-05-21 13:06:08,123][evaluator][INFO] - Result for metric forget_Q_A_ROUGE: 0.17469059451335844 +[2026-05-21 13:06:09,777][metrics][INFO] - Evaluating retain_Q_A_Prob +[2026-05-21 13:06:17,186][metrics][INFO] - Evaluating retain_Q_A_ROUGE +[2026-05-21 13:08:23,471][metrics][INFO] - Evaluating retain_Q_A_PARA_Prob +[2026-05-21 13:08:31,153][metrics][INFO] - Evaluating retain_Q_A_PERT_Prob +[2026-05-21 13:09:00,265][metrics][INFO] - Evaluating retain_Truth_Ratio +[2026-05-21 13:09:01,944][metrics][INFO] - Evaluating ra_Q_A_Prob +[2026-05-21 13:09:04,573][metrics][INFO] - Evaluating ra_Q_A_PERT_Prob +[2026-05-21 13:09:07,999][metrics][INFO] - Evaluating ra_Q_A_Prob_normalised +[2026-05-21 13:09:09,674][metrics][INFO] - Evaluating ra_Q_A_ROUGE +[2026-05-21 13:09:45,248][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_Prob, already evaluated. +[2026-05-21 13:09:45,249][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_PERT_Prob, already evaluated. +[2026-05-21 13:09:45,249][metrics][INFO] - Evaluating ra_Truth_Ratio +[2026-05-21 13:09:46,946][metrics][INFO] - Evaluating wf_Q_A_Prob +[2026-05-21 13:09:49,056][metrics][INFO] - Evaluating wf_Q_A_PERT_Prob +[2026-05-21 13:09:52,602][metrics][INFO] - Evaluating wf_Q_A_Prob_normalised +[2026-05-21 13:09:54,048][metrics][INFO] - Evaluating wf_Q_A_ROUGE +[2026-05-21 13:10:30,231][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_Prob, already evaluated. +[2026-05-21 13:10:30,231][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_PERT_Prob, already evaluated. +[2026-05-21 13:10:30,231][metrics][INFO] - Evaluating wf_Truth_Ratio +[2026-05-21 13:10:30,232][metrics][INFO] - Evaluating model_utility +[2026-05-21 13:10:30,232][evaluator][INFO] - Result for metric model_utility: 0.02489956345780598 +[2026-05-21 13:10:33,282][metrics][INFO] - Evaluating mia_min_k +[2026-05-21 13:10:36,848][metrics][INFO] - Evaluating privleak +[2026-05-21 13:10:36,849][metrics][WARNING] - retain_model_logs evals not provided for privleak, using default retain auc of 0.5 +[2026-05-21 13:10:36,849][evaluator][INFO] - Result for metric privleak: -13.848749997230259 +[2026-05-21 13:10:38,234][metrics][INFO] - Evaluating extraction_strength +[2026-05-21 13:10:56,184][evaluator][INFO] - Result for metric extraction_strength: 0.03481065013680257 +[2026-05-21 13:11:08,088][evaluator][INFO] - ***** Running TOFU evaluation suite ***** +[2026-05-21 13:11:08,088][evaluator][INFO] - Fine-grained evaluations will be saved to: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-225/evals/TOFU_EVAL.json +[2026-05-21 13:11:08,088][evaluator][INFO] - Aggregated evaluations will be summarised in: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-225/evals/TOFU_SUMMARY.json +[2026-05-21 13:11:09,829][metrics][INFO] - Evaluating forget_Q_A_PARA_Prob +[2026-05-21 13:11:17,751][metrics][INFO] - Evaluating forget_Q_A_PERT_Prob +[2026-05-21 13:11:46,542][metrics][INFO] - Evaluating forget_truth_ratio +[2026-05-21 13:11:46,543][evaluator][INFO] - Result for metric forget_truth_ratio: 0.6661828793220076 +[2026-05-21 13:11:46,550][metrics][INFO] - Skipping forget_quality's precompute forget_truth_ratio, already evaluated. +[2026-05-21 13:11:46,550][metrics][INFO] - Evaluating forget_quality +[2026-05-21 13:11:46,550][metrics][WARNING] - retain_model_logs not provided in reference_logs, setting forget_quality to None +[2026-05-21 13:11:46,550][evaluator][INFO] - Result for metric forget_quality: None +[2026-05-21 13:11:48,331][metrics][INFO] - Evaluating forget_Q_A_Prob +[2026-05-21 13:11:54,570][evaluator][INFO] - Result for metric forget_Q_A_Prob: 0.02249195015472651 +[2026-05-21 13:11:56,226][metrics][INFO] - Evaluating forget_Q_A_ROUGE +[2026-05-21 13:13:55,029][evaluator][INFO] - Result for metric forget_Q_A_ROUGE: 0.16997095669432055 +[2026-05-21 13:13:56,746][metrics][INFO] - Evaluating retain_Q_A_Prob +[2026-05-21 13:14:04,157][metrics][INFO] - Evaluating retain_Q_A_ROUGE +[2026-05-21 13:16:10,552][metrics][INFO] - Evaluating retain_Q_A_PARA_Prob +[2026-05-21 13:16:18,183][metrics][INFO] - Evaluating retain_Q_A_PERT_Prob +[2026-05-21 13:16:47,385][metrics][INFO] - Evaluating retain_Truth_Ratio +[2026-05-21 13:16:49,063][metrics][INFO] - Evaluating ra_Q_A_Prob +[2026-05-21 13:16:51,612][metrics][INFO] - Evaluating ra_Q_A_PERT_Prob +[2026-05-21 13:16:55,772][metrics][INFO] - Evaluating ra_Q_A_Prob_normalised +[2026-05-21 13:16:57,083][metrics][INFO] - Evaluating ra_Q_A_ROUGE +[2026-05-21 13:17:36,254][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_Prob, already evaluated. +[2026-05-21 13:17:36,254][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_PERT_Prob, already evaluated. +[2026-05-21 13:17:36,254][metrics][INFO] - Evaluating ra_Truth_Ratio +[2026-05-21 13:17:37,889][metrics][INFO] - Evaluating wf_Q_A_Prob +[2026-05-21 13:17:40,234][metrics][INFO] - Evaluating wf_Q_A_PERT_Prob +[2026-05-21 13:17:43,663][metrics][INFO] - Evaluating wf_Q_A_Prob_normalised +[2026-05-21 13:17:45,073][metrics][INFO] - Evaluating wf_Q_A_ROUGE +[2026-05-21 13:18:21,334][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_Prob, already evaluated. +[2026-05-21 13:18:21,334][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_PERT_Prob, already evaluated. +[2026-05-21 13:18:21,334][metrics][INFO] - Evaluating wf_Truth_Ratio +[2026-05-21 13:18:21,335][metrics][INFO] - Evaluating model_utility +[2026-05-21 13:18:21,335][evaluator][INFO] - Result for metric model_utility: 0.034445471917355024 +[2026-05-21 13:18:24,581][metrics][INFO] - Evaluating mia_min_k +[2026-05-21 13:18:30,118][metrics][INFO] - Evaluating privleak +[2026-05-21 13:18:30,118][metrics][WARNING] - retain_model_logs evals not provided for privleak, using default retain auc of 0.5 +[2026-05-21 13:18:30,118][evaluator][INFO] - Result for metric privleak: -13.99624999720075 +[2026-05-21 13:18:31,791][metrics][INFO] - Evaluating extraction_strength +[2026-05-21 13:18:33,441][evaluator][INFO] - Result for metric extraction_strength: 0.03500545533160776 +[2026-05-21 13:18:48,997][evaluator][INFO] - ***** Running TOFU evaluation suite ***** +[2026-05-21 13:18:48,998][evaluator][INFO] - Fine-grained evaluations will be saved to: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-250/evals/TOFU_EVAL.json +[2026-05-21 13:18:48,998][evaluator][INFO] - Aggregated evaluations will be summarised in: saves/unlearn/qat-baseline/tofu_Llama-3.2-1B-Instruct_forget10_RMU_qat-int4/checkpoint-250/evals/TOFU_SUMMARY.json +[2026-05-21 13:18:50,692][metrics][INFO] - Evaluating forget_Q_A_PARA_Prob +[2026-05-21 13:18:58,986][metrics][INFO] - Evaluating forget_Q_A_PERT_Prob +[2026-05-21 13:19:25,257][metrics][INFO] - Evaluating forget_truth_ratio +[2026-05-21 13:19:25,258][evaluator][INFO] - Result for metric forget_truth_ratio: 0.6682139209275343 +[2026-05-21 13:19:25,264][metrics][INFO] - Skipping forget_quality's precompute forget_truth_ratio, already evaluated. +[2026-05-21 13:19:25,265][metrics][INFO] - Evaluating forget_quality +[2026-05-21 13:19:25,265][metrics][WARNING] - retain_model_logs not provided in reference_logs, setting forget_quality to None +[2026-05-21 13:19:25,265][evaluator][INFO] - Result for metric forget_quality: None +[2026-05-21 13:19:26,973][metrics][INFO] - Evaluating forget_Q_A_Prob +[2026-05-21 13:19:32,982][evaluator][INFO] - Result for metric forget_Q_A_Prob: 0.022312334740927326 +[2026-05-21 13:19:34,673][metrics][INFO] - Evaluating forget_Q_A_ROUGE +[2026-05-21 13:21:16,645][evaluator][INFO] - Result for metric forget_Q_A_ROUGE: 0.16821955009936282 +[2026-05-21 13:21:18,356][metrics][INFO] - Evaluating retain_Q_A_Prob +[2026-05-21 13:21:25,924][metrics][INFO] - Evaluating retain_Q_A_ROUGE +[2026-05-21 13:23:30,694][metrics][INFO] - Evaluating retain_Q_A_PARA_Prob +[2026-05-21 13:23:38,159][metrics][INFO] - Evaluating retain_Q_A_PERT_Prob +[2026-05-21 13:24:07,357][metrics][INFO] - Evaluating retain_Truth_Ratio +[2026-05-21 13:24:09,157][metrics][INFO] - Evaluating ra_Q_A_Prob +[2026-05-21 13:24:11,661][metrics][INFO] - Evaluating ra_Q_A_PERT_Prob +[2026-05-21 13:24:15,807][metrics][INFO] - Evaluating ra_Q_A_Prob_normalised +[2026-05-21 13:24:17,063][metrics][INFO] - Evaluating ra_Q_A_ROUGE +[2026-05-21 13:24:56,249][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_Prob, already evaluated. +[2026-05-21 13:24:56,250][metrics][INFO] - Skipping ra_Truth_Ratio's precompute ra_Q_A_PERT_Prob, already evaluated. +[2026-05-21 13:24:56,250][metrics][INFO] - Evaluating ra_Truth_Ratio +[2026-05-21 13:24:57,929][metrics][INFO] - Evaluating wf_Q_A_Prob +[2026-05-21 13:25:00,268][metrics][INFO] - Evaluating wf_Q_A_PERT_Prob +[2026-05-21 13:25:03,792][metrics][INFO] - Evaluating wf_Q_A_Prob_normalised +[2026-05-21 13:25:05,243][metrics][INFO] - Evaluating wf_Q_A_ROUGE +[2026-05-21 13:25:41,021][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_Prob, already evaluated. +[2026-05-21 13:25:41,021][metrics][INFO] - Skipping wf_Truth_Ratio's precompute wf_Q_A_PERT_Prob, already evaluated. +[2026-05-21 13:25:41,021][metrics][INFO] - Evaluating wf_Truth_Ratio +[2026-05-21 13:25:41,021][metrics][INFO] - Evaluating model_utility +[2026-05-21 13:25:41,022][evaluator][INFO] - Result for metric model_utility: 0.024945798930326533 +[2026-05-21 13:25:44,127][metrics][INFO] - Evaluating mia_min_k +[2026-05-21 13:25:49,445][metrics][INFO] - Evaluating privleak +[2026-05-21 13:25:49,445][metrics][WARNING] - retain_model_logs evals not provided for privleak, using default retain auc of 0.5 +[2026-05-21 13:25:49,445][evaluator][INFO] - Result for metric privleak: -14.171249997165742 +[2026-05-21 13:25:52,550][metrics][INFO] - Evaluating extraction_strength +[2026-05-21 13:26:14,618][evaluator][INFO] - Result for metric extraction_strength: 0.034667360093512525 diff --git a/config.json b/config.json new file mode 100644 index 0000000..429177f --- /dev/null +++ b/config.json @@ -0,0 +1,39 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": [ + 128001, + 128008, + 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