| ===== MB round-1 task=hs2gemma anchor=/NHNHOME/AIPR/sjkim/policy_bases/models--google--gemma-2-9b-it/snapshots/11c9b309abf73637e4b6f9a3fa1e92e615547819/ weights={"helpfulness":0.2500,"correctness":0.2500,"coherence":0.2500,"conciseness":0.2500} ===== |
| manifest 2500 |
| allresp built |
| scores combined {'pol_42': 2500, 'pol_43': 2500, 'base_44': 2500, 'base_45': 2500} |
| {"surplus(2P-1)": {"helpfulness": -0.035, "correctness": -0.033, "coherence": -0.028, "conciseness": 0.001}, "P(pi>mu)": {"helpfulness": 0.482, "correctness": 0.484, "coherence": 0.486, "conciseness": 0.5}, "w_nbs": {"helpfulness": 0.333, "correctness": 0.333, "coherence": 0.333, "conciseness": 0.001}, "w_ks": {"helpfulness": 0.251, "correctness": 0.251, "coherence": 0.251, "conciseness": 0.247}, "worst": "helpfulness", "n": 5000} |
| RMPOOL_hs2gemma_DONE |
| [build_bon] 2500 pairs (train 2250/test 250) weights {'helpfulness': 0.25, 'correctness': 0.25, 'coherence': 0.25, 'conciseness': 0.25} |
| [prep hs2gemma EXP=exp/rmpool_mbgsp1] objs: helpfulness correctness coherence conciseness parent: /NHNHOME/AIPR/sjkim/policy_bases/models--google--gemma-2-9b-it/snapshots/11c9b309abf73637e4b6f9a3fa1e92e615547819/ |
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| [prep] pre_pref + pre_htobj built |
| dpo htmnpo_coherence htmnpo_conciseness htmnpo_correctness htmnpo_helpfulness inpo ipo ks maxmin nbs simpo unif |
| TRAIN_DONE aiprlab-polymer |
| MB1_TRAIN_FAIL |
| ===== MB round-1 task=hs2gemma anchor=/NHNHOME/AIPR/sjkim/policy_bases/models--google--gemma-2-9b-it/snapshots/11c9b309abf73637e4b6f9a3fa1e92e615547819/ weights={"helpfulness":0.2500,"correctness":0.2500,"coherence":0.2500,"conciseness":0.2500} ===== |
| allresp built |
| scores combined {'pol_42': 2500, 'pol_43': 2500, 'base_44': 2500, 'base_45': 2500} |
| {"surplus(2P-1)": {"helpfulness": -0.035, "correctness": -0.033, "coherence": -0.028, "conciseness": 0.001}, "P(pi>mu)": {"helpfulness": 0.482, "correctness": 0.484, "coherence": 0.486, "conciseness": 0.5}, "w_nbs": {"helpfulness": 0.333, "correctness": 0.333, "coherence": 0.333, "conciseness": 0.001}, "w_ks": {"helpfulness": 0.251, "correctness": 0.251, "coherence": 0.251, "conciseness": 0.247}, "worst": "helpfulness", "n": 5000} |
| RMPOOL_hs2gemma_DONE |
| [build_bon] 2500 pairs (train 2250/test 250) weights {'helpfulness': 0.25, 'correctness': 0.25, 'coherence': 0.25, 'conciseness': 0.25} |
| [prep hs2gemma EXP=exp/rmpool_mbgsp1] objs: helpfulness correctness coherence conciseness parent: /NHNHOME/AIPR/sjkim/policy_bases/models--google--gemma-2-9b-it/snapshots/11c9b309abf73637e4b6f9a3fa1e92e615547819/ |
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| Saving the dataset (1/1 shards): 100%|██████████| 250/250 [00:00<00:00, 24626.02 examples/s] |
| [prep] pre_pref + pre_htobj built |
| dpo htmnpo_coherence htmnpo_conciseness htmnpo_correctness htmnpo_helpfulness inpo ipo ks maxmin nbs simpo unif |
| TRAIN_DONE aiprlab-polymer |
| MB1_TRAIN_FAIL |
| ===== MB round-1 task=hs2gemma anchor=/NHNHOME/AIPR/sjkim/policy_bases/models--google--gemma-2-9b-it/snapshots/11c9b309abf73637e4b6f9a3fa1e92e615547819/ weights={"helpfulness":0.2500,"correctness":0.2500,"coherence":0.2500,"conciseness":0.2500} ===== |
| allresp built |
| scores combined {'pol_42': 2500, 'pol_43': 2500, 'base_44': 2500, 'base_45': 2500} |
| {"surplus(2P-1)": {"helpfulness": -0.035, "correctness": -0.033, "coherence": -0.028, "conciseness": 0.001}, "P(pi>mu)": {"helpfulness": 0.482, "correctness": 0.484, "coherence": 0.486, "conciseness": 0.5}, "w_nbs": {"helpfulness": 0.333, "correctness": 0.333, "coherence": 0.333, "conciseness": 0.001}, "w_ks": {"helpfulness": 0.251, "correctness": 0.251, "coherence": 0.251, "conciseness": 0.247}, "worst": "helpfulness", "n": 5000} |
| RMPOOL_hs2gemma_DONE |
| [build_bon] 2500 pairs (train 2250/test 250) weights {'helpfulness': 0.25, 'correctness': 0.25, 'coherence': 0.25, 'conciseness': 0.25} |
| [prep hs2gemma EXP=exp/rmpool_mbgsp1] objs: helpfulness correctness coherence conciseness parent: /NHNHOME/AIPR/sjkim/policy_bases/models--google--gemma-2-9b-it/snapshots/11c9b309abf73637e4b6f9a3fa1e92e615547819/ |
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| [prep] pre_pref + pre_htobj built |
| dpo htmnpo_coherence htmnpo_conciseness htmnpo_correctness htmnpo_helpfulness inpo ipo ks maxmin nbs simpo unif |
| TRAIN_DONE aiprlab-polymer |
| MB1_TRAIN_FAIL |
| ===== MB round-1 task=hs2gemma anchor=/NHNHOME/AIPR/sjkim/policy_bases/models--google--gemma-2-9b-it/snapshots/11c9b309abf73637e4b6f9a3fa1e92e615547819/ weights={"helpfulness":0.2500,"correctness":0.2500,"coherence":0.2500,"conciseness":0.2500} ===== |
| allresp built |
| scores combined {'pol_42': 2500, 'pol_43': 2500, 'base_44': 2500, 'base_45': 2500} |
| {"surplus(2P-1)": {"helpfulness": -0.035, "correctness": -0.033, "coherence": -0.028, "conciseness": 0.001}, "P(pi>mu)": {"helpfulness": 0.482, "correctness": 0.484, "coherence": 0.486, "conciseness": 0.5}, "w_nbs": {"helpfulness": 0.333, "correctness": 0.333, "coherence": 0.333, "conciseness": 0.001}, "w_ks": {"helpfulness": 0.251, "correctness": 0.251, "coherence": 0.251, "conciseness": 0.247}, "worst": "helpfulness", "n": 5000} |
| RMPOOL_hs2gemma_DONE |
| [build_bon] 2500 pairs (train 2250/test 250) weights {'helpfulness': 0.25, 'correctness': 0.25, 'coherence': 0.25, 'conciseness': 0.25} |
| [prep hs2gemma EXP=exp/rmpool_mbgsp1] objs: helpfulness correctness coherence conciseness parent: /NHNHOME/AIPR/sjkim/policy_bases/models--google--gemma-2-9b-it/snapshots/11c9b309abf73637e4b6f9a3fa1e92e615547819/ |
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| Saving the dataset (1/1 shards): 100%|██████████| 250/250 [00:00<00:00, 24585.03 examples/s] |
| [prep] pre_pref + pre_htobj built |
| dpo htmnpo_coherence htmnpo_conciseness htmnpo_correctness htmnpo_helpfulness inpo ipo ks maxmin nbs simpo unif |
| TRAIN_DONE aiprlab-polymer |
| MB1_TRAIN_FAIL |
| ===== MB round-1 task=hs2gemma anchor=/NHNHOME/AIPR/sjkim/policy_bases/models--google--gemma-2-9b-it/snapshots/11c9b309abf73637e4b6f9a3fa1e92e615547819/ weights={"helpfulness":0.2500,"correctness":0.2500,"coherence":0.2500,"conciseness":0.2500} ===== |
| allresp built |
| scores combined {'pol_42': 2500, 'pol_43': 2500, 'base_44': 2500, 'base_45': 2500} |
| {"surplus(2P-1)": {"helpfulness": -0.035, "correctness": -0.033, "coherence": -0.028, "conciseness": 0.001}, "P(pi>mu)": {"helpfulness": 0.482, "correctness": 0.484, "coherence": 0.486, "conciseness": 0.5}, "w_nbs": {"helpfulness": 0.333, "correctness": 0.333, "coherence": 0.333, "conciseness": 0.001}, "w_ks": {"helpfulness": 0.251, "correctness": 0.251, "coherence": 0.251, "conciseness": 0.247}, "worst": "helpfulness", "n": 5000} |
| RMPOOL_hs2gemma_DONE |
| [build_bon] 2500 pairs (train 2250/test 250) weights {'helpfulness': 0.25, 'correctness': 0.25, 'coherence': 0.25, 'conciseness': 0.25} |
| [prep hs2gemma EXP=exp/rmpool_mbgsp1] objs: helpfulness correctness coherence conciseness parent: /NHNHOME/AIPR/sjkim/policy_bases/models--google--gemma-2-9b-it/snapshots/11c9b309abf73637e4b6f9a3fa1e92e615547819/ |
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| Saving the dataset (1/1 shards): 100%|██████████| 250/250 [00:00<00:00, 25523.98 examples/s] |
| [prep] pre_pref + pre_htobj built |
| dpo htmnpo_coherence htmnpo_conciseness htmnpo_correctness htmnpo_helpfulness inpo ipo ks maxmin nbs simpo unif |
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