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dense_reward_trainer_final_opt__NumTrainEpochs5_SaveStrategiesno_reward_modeling_anthropic_hh

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  1. README.md +118 -0
  2. config.json +37 -0
  3. merges.txt +0 -0
  4. model-00001-of-00002.safetensors +3 -0
  5. model-00002-of-00002.safetensors +3 -0
  6. model.safetensors.index.json +397 -0
  7. special_tokens_map.json +30 -0
  8. tokenizer.json +0 -0
  9. tokenizer_config.json +30 -0
  10. training_args.bin +3 -0
  11. vocab.json +0 -0
README.md ADDED
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+ ---
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+ license: other
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+ base_model: facebook/opt-1.3b
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: dense_reward_trainer_final_opt__NumTrainEpochs5_SaveStrategiesno_reward_modeling_anthropic_hh
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # dense_reward_trainer_final_opt__NumTrainEpochs5_SaveStrategiesno_reward_modeling_anthropic_hh
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+
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+ This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.4124
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+ - Accuracy: 0.6660
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+ - Train Rewards/chosen: 9.2061
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+ - Train Rewards/rejected: -9.4536
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+ - Train Rewards/accuracies: 0.9844
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+ - Train Rewards/margins: 18.6597
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+ - Train Nll Loss: 2.1547
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+ - Train Logit Total Loss: 0.0587
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+ - Train Logit Loss: 0.0375
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+ - Rewards/chosen: 3.4303
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+ - Rewards/rejected: -2.3575
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+ - Rewards/accuracies: 0.6484
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+ - Rewards/margins: 5.7878
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+ - Nll Loss: 2.1950
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+ - Logit Total Loss: 2.4421
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+ - Logit Loss: 2.4446
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 1.41e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 16
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Nll Loss | Logit Total Loss | Logit Loss |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------:|:-----------------:|:-----------:|
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+ | 0.7077 | 0.11 | 100 | 0.6897 | 0.6165 | -1.6378 | -1.8001 | 0.6016 | 0.1622 | 2.8092 | 0.6881 | 0.6667 |
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+ | 0.7117 | 0.23 | 200 | 0.6764 | 0.6103 | -2.8148 | -3.0536 | 0.5964 | 0.2388 | 2.8927 | 0.6746 | 0.6522 |
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+ | 0.6502 | 0.34 | 300 | 0.6626 | 0.6536 | -0.8018 | -1.1645 | 0.6399 | 0.3627 | 2.9696 | 0.6611 | 0.6377 |
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+ | 0.655 | 0.46 | 400 | 0.6503 | 0.6144 | -1.5457 | -1.9648 | 0.5984 | 0.4191 | 2.7773 | 0.6489 | 0.6274 |
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+ | 0.6467 | 0.57 | 500 | 0.6653 | 0.6165 | -0.9541 | -1.3483 | 0.6036 | 0.3942 | 2.8139 | 0.6643 | 0.6426 |
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+ | 0.6694 | 0.69 | 600 | 0.6432 | 0.6392 | -1.5917 | -1.9439 | 0.6278 | 0.3522 | 2.7779 | 0.6426 | 0.6211 |
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+ | 0.6753 | 0.8 | 700 | 0.6494 | 0.6371 | -1.3508 | -1.7191 | 0.6246 | 0.3683 | 2.8056 | 0.6474 | 0.6256 |
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+ | 0.6806 | 0.91 | 800 | 0.6449 | 0.6103 | -1.4576 | -1.8165 | 0.6004 | 0.3589 | 2.7215 | 0.6424 | 0.6214 |
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+ | 0.5434 | 1.03 | 900 | 0.6827 | 0.6557 | -0.8965 | -1.6611 | 0.6468 | 0.7645 | 2.6762 | 0.6816 | 0.6615 |
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+ | 0.5448 | 1.14 | 1000 | 0.7194 | 0.6392 | -0.8661 | -1.8265 | 0.6266 | 0.9604 | 2.6214 | 0.7184 | 0.6992 |
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+ | 0.5129 | 1.26 | 1100 | 0.7990 | 0.6289 | 1.3108 | 0.2390 | 0.6165 | 1.0718 | 2.6526 | 0.7966 | 0.7779 |
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+ | 0.5033 | 1.37 | 1200 | 0.6888 | 0.6557 | -0.9571 | -1.8601 | 0.6488 | 0.9030 | 2.6263 | 0.6868 | 0.6672 |
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+ | 0.404 | 1.49 | 1300 | 0.7422 | 0.6309 | -1.1408 | -2.0297 | 0.6226 | 0.8890 | 2.6046 | 0.7348 | 0.7159 |
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+ | 0.5512 | 1.6 | 1400 | 0.6762 | 0.6474 | -2.5166 | -3.3023 | 0.6327 | 0.7857 | 2.5872 | 0.6766 | 0.6573 |
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+ | 0.4558 | 1.71 | 1500 | 0.6843 | 0.6619 | -2.3183 | -3.2412 | 0.6476 | 0.9229 | 2.5268 | 0.6811 | 0.6625 |
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+ | 0.5184 | 1.83 | 1600 | 0.7135 | 0.6557 | -1.5991 | -2.5538 | 0.6456 | 0.9547 | 2.5671 | 0.7179 | 0.6992 |
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+ | 0.4213 | 1.94 | 1700 | 0.7220 | 0.6495 | -1.3947 | -2.4198 | 0.6395 | 1.0251 | 2.5040 | 0.7198 | 0.7018 |
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+ | 0.1508 | 2.06 | 1800 | 1.0827 | 0.6598 | 2.5282 | 0.2534 | 0.6476 | 2.2748 | 2.6437 | 1.0758 | 1.0599 |
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+ | 0.1216 | 2.17 | 1900 | 1.1376 | 0.6474 | -0.0750 | -2.1523 | 0.6302 | 2.0773 | 2.5506 | 1.1502 | 1.1361 |
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+ | 0.1044 | 2.29 | 2000 | 1.4682 | 0.6722 | -0.4860 | -3.5268 | 0.6577 | 3.0408 | 2.5292 | 1.4836 | 1.4730 |
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+ | 0.0952 | 2.4 | 2100 | 1.6303 | 0.6639 | 1.9842 | -1.3673 | 0.6444 | 3.3515 | 2.5293 | 1.6377 | 1.6287 |
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+ | 0.1951 | 2.51 | 2200 | 1.1515 | 0.6784 | -0.0674 | -2.4660 | 0.6637 | 2.3985 | 2.4589 | 1.1463 | 1.1331 |
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+ | 0.1119 | 2.63 | 2300 | 1.3845 | 0.6722 | 4.4149 | 1.2669 | 0.6548 | 3.1480 | 2.4797 | 1.3869 | 1.3759 |
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+ | 0.1613 | 2.74 | 2400 | 1.1948 | 0.6536 | -4.3162 | -7.1133 | 0.6367 | 2.7971 | 2.4661 | 1.2014 | 1.1887 |
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+ | 0.1408 | 2.86 | 2500 | 1.4167 | 0.6557 | -3.1501 | -6.3592 | 0.6415 | 3.2091 | 2.4591 | 1.4242 | 1.4137 |
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+ | 0.2694 | 2.97 | 2600 | 1.2168 | 0.6536 | 0.5185 | -2.2531 | 0.6395 | 2.7716 | 2.4397 | 1.2074 | 1.1949 |
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+ | 0.1184 | 3.09 | 2700 | 1.6729 | 0.6412 | 0.5427 | -3.2829 | 0.6315 | 3.8257 | 2.4188 | 1.6627 | 1.6551 |
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+ | 0.1004 | 3.2 | 2800 | 1.8768 | 0.6742 | 3.9205 | -0.6543 | 0.6629 | 4.5748 | 2.3906 | 1.8625 | 1.8572 |
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+ | 0.1029 | 3.31 | 2900 | 1.7461 | 0.6619 | 0.1775 | -4.2079 | 0.6496 | 4.3854 | 2.3534 | 1.7356 | 1.7294 |
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+ | 0.0401 | 3.43 | 3000 | 1.9949 | 0.6825 | 3.6497 | -1.3819 | 0.6698 | 5.0317 | 2.3327 | 1.9902 | 1.9868 |
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+ | 0.04 | 3.54 | 3100 | 2.0206 | 0.6763 | -0.5106 | -5.0903 | 0.6597 | 4.5798 | 2.3202 | 2.0224 | 2.0194 |
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+ | 0.1035 | 3.66 | 3200 | 2.1971 | 0.6660 | 2.3511 | -2.5645 | 0.6536 | 4.9156 | 2.3137 | 2.2218 | 2.2209 |
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+ | 0.0589 | 3.77 | 3300 | 2.1599 | 0.6412 | 2.0054 | -2.7469 | 0.6262 | 4.7523 | 2.2936 | 2.1789 | 2.1777 |
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+ | 0.084 | 3.89 | 3400 | 2.2096 | 0.6598 | 1.7952 | -3.0061 | 0.6391 | 4.8013 | 2.2833 | 2.2386 | 2.2382 |
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+ | 0.063 | 4.0 | 3500 | 2.2277 | 0.6660 | 4.2291 | -0.8513 | 0.6484 | 5.0805 | 2.2693 | 2.2539 | 2.2537 |
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+ | 0.065 | 4.11 | 3600 | 2.3431 | 0.6598 | 2.1719 | -3.1923 | 0.6444 | 5.3642 | 2.2499 | 2.3575 | 2.3585 |
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+ | 0.0453 | 4.23 | 3700 | 2.4069 | 0.6474 | 5.6839 | 0.2229 | 0.6335 | 5.4609 | 2.2344 | 2.4327 | 2.4347 |
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+ | 0.0377 | 4.34 | 3800 | 2.4983 | 0.6557 | 2.7785 | -2.9928 | 0.6355 | 5.7714 | 2.2258 | 2.5397 | 2.5429 |
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+ | 0.0559 | 4.46 | 3900 | 2.4027 | 0.6536 | 2.8063 | -2.8587 | 0.6375 | 5.6650 | 2.2135 | 2.4278 | 2.4299 |
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+ | 0.0219 | 4.57 | 4000 | 2.4322 | 0.6598 | 3.9024 | -1.8412 | 0.6435 | 5.7436 | 2.2081 | 2.4805 | 2.4832 |
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+ | 0.09 | 4.69 | 4100 | 2.4041 | 0.6680 | 3.7769 | -1.9890 | 0.6496 | 5.7659 | 2.2011 | 2.4248 | 2.4271 |
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+ | 0.0897 | 4.8 | 4200 | 2.3727 | 0.6722 | 2.7679 | -3.0182 | 0.6524 | 5.7861 | 2.1974 | 2.3815 | 2.3833 |
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+ | 0.0474 | 4.91 | 4300 | 2.4124 | 0.6660 | 3.4303 | -2.3575 | 0.6484 | 5.7878 | 2.1950 | 2.4421 | 2.4446 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.37.2
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.21.0
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+ - Tokenizers 0.15.2
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