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sheng22213/genarm-h0p01-c0175-eval-code
genarm-h0p01-c0175-eval-code is a machine learning model from sheng22213. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This repository packages the runnable code for the h0p01 + c=0.175 GenARM evaluation on the 500-prompt non-overlap test set.
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From the Hugging Face model README
This repository packages the runnable code for the h0p01 + c=0.175 GenARM evaluation on the 500-prompt non-overlap test set.
dataset/evaluation_prompts-full-728_without_train_overlap_500.json0alpaca-7b-reproducedarm_beta_0p5_masked_round4_extreme/final_checkpointarm_beta_0p01_masked_round4_extreme/final_checkpointalpha_help + 0.175 * alpha_harm = 1A_t = {v | p_base(v) >= threshold}A_t:
S(v) = log p_base(v) + alpha_help * log p_help(v) + alpha_harm * log p_harm(v)S(v) restricted to A_tMeta-Llama-3-70B-InstructSet these environment variables on the target machine:
export BASE_MODEL=/path/to/alpaca-7b-reproduced
export HELPFUL_ADAPTER=/path/to/arm_beta_0p5_masked_round4_extreme/final_checkpoint
export HARMLESS_ADAPTER=/path/to/arm_beta_0p01_masked_round4_extreme/final_checkpoint
export JUDGE_MODEL=/path/to/Meta-Llama-3-70B-Instruct
The repo includes baseline/base_generation_seed0.json for pairwise evaluation. Override BASE_GENERATION=/path/to/base/generation.json only if you want to compare against a different base output.
bash scripts/run_h0p01_c0175_point.sh 0.7 scheme_a_threshold_0p0008
This computes alpha_harm = (1 - alpha_help) / 0.175, then runs generation, pairwise evaluation, humanness evaluation, and summary creation.
The default full set is 11 points:
bash scripts/submit_pbs_h0p01_c0175.sh
The default methods are:
scheme_a_threshold_0p0008: alpha_help = 0, 0.1, ..., 1scheme_a_threshold_0p0005: alpha_help = 0, 0.7, 0.8, 1scheme_a_threshold_0p0003: alpha_help = 0, 0.7, 0.8, 1scheme_a_threshold_0p001: alpha_help = 0, 0.7, 0.8, 1Override with environment variables if needed:
METHODS="scheme_a_threshold_0p0008" ALPHA_HELPS="0 0.7 0.8 1" bash scripts/submit_pbs_h0p01_c0175.sh
Results are written under:
outputs/h0p01_c0175_seed0/
Each point produces:
generation.jsonsummary.jsonThe main summary metrics are:
pairwise.win_halfTie_helpfulnesspairwise.win_halfTie_harmlessnesshumanness.avg_score_humannessThe packaged runner uses two GPUs for generation and sets the judge to auto device mapping across both GPUs by default. On a single machine without PBS, run scripts/run_h0p01_c0175_point.sh directly.