NicholasCorrado
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Model save
Browse files- README.md +100 -0
- all_results.json +9 -0
- generation_config.json +6 -0
- train_results.json +9 -0
README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: alignment-handbook/zephyr-7b-sft-full
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tags:
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- trl
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- dpo
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- generated_from_trainer
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model-index:
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- name: zephyr-7b-uf-rlced-conifer-1e2e-group-dpo-2e
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results: []
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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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# zephyr-7b-uf-rlced-conifer-1e2e-group-dpo-2e
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This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2626
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- Rewards/chosen: -2.1843
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- Rewards/rejected: -5.4288
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- Rewards/accuracies: 0.8684
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- Rewards/margins: 3.2445
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- Logps/rejected: -946.6157
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- Logps/chosen: -610.9032
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- Logits/rejected: 1.2318
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- Logits/chosen: -0.7806
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- Excess Loss: 0.0374
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- Alpha 0 Uf: 0.8470
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- Alpha 1 Rlced Conifer: 0.1530
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- Rewards/chosen 1 Rlced Conifer: -2.2281
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- Rewards/rejected 1 Rlced Conifer: -6.0246
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- Rewards/accuracies 1 Rlced Conifer: 0.8987
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- Rewards/margins 1 Rlced Conifer: 3.7965
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- Logps/rejected 1 Rlced Conifer: -1049.9939
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- Logps/chosen 1 Rlced Conifer: -646.3860
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- Logits/rejected 1 Rlced Conifer: 1.1158
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- Logits/chosen 1 Rlced Conifer: -0.9982
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- Task Loss 1 Rlced Conifer: 0.2102
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- Task Excess Loss 1 Rlced Conifer: 0.0475
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- Rewards/chosen 0 Uf: -1.9978
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- Rewards/rejected 0 Uf: -3.3091
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- Rewards/accuracies 0 Uf: 0.7603
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- Rewards/margins 0 Uf: 1.3113
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- Logps/rejected 0 Uf: -572.5212
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- Logps/chosen 0 Uf: -489.0419
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- Logits/rejected 0 Uf: 1.8243
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- Logits/chosen 0 Uf: -0.1004
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- Task Loss 0 Uf: 0.4944
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- Task Excess Loss 0 Uf: 0.0469
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-07
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 256
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- total_eval_batch_size: 64
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | Excess Loss | Alpha 0 Uf | Alpha 1 Rlced Conifer | Rewards/chosen 1 Rlced Conifer | Rewards/rejected 1 Rlced Conifer | Rewards/accuracies 1 Rlced Conifer | Rewards/margins 1 Rlced Conifer | Logps/rejected 1 Rlced Conifer | Logps/chosen 1 Rlced Conifer | Logits/rejected 1 Rlced Conifer | Logits/chosen 1 Rlced Conifer | Task Loss 1 Rlced Conifer | Task Excess Loss 1 Rlced Conifer | Rewards/chosen 0 Uf | Rewards/rejected 0 Uf | Rewards/accuracies 0 Uf | Rewards/margins 0 Uf | Logps/rejected 0 Uf | Logps/chosen 0 Uf | Logits/rejected 0 Uf | Logits/chosen 0 Uf | Task Loss 0 Uf | Task Excess Loss 0 Uf |
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|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|:-----------:|:----------:|:---------------------:|:------------------------------:|:--------------------------------:|:----------------------------------:|:-------------------------------:|:------------------------------:|:----------------------------:|:-------------------------------:|:-----------------------------:|:-------------------------:|:--------------------------------:|:-------------------:|:---------------------:|:-----------------------:|:--------------------:|:-------------------:|:-----------------:|:--------------------:|:------------------:|:--------------:|:---------------------:|
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| 0.1953 | 0.4997 | 360 | 0.3535 | -1.5938 | -3.1996 | 0.8402 | 1.6058 | -723.6984 | -551.8521 | 0.1112 | -0.7863 | 0.1136 | 0.9694 | 0.0306 | -1.5989 | -3.4179 | 0.8677 | 1.8190 | -789.3262 | -583.4747 | -0.1145 | -0.9516 | 0.3087 | 0.1414 | -1.5520 | -2.3972 | 0.7448 | 0.8452 | -481.3242 | -444.4588 | 1.0137 | -0.2527 | 0.5289 | 0.0768 |
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| 0.1537 | 0.9993 | 720 | 0.3329 | -1.4289 | -3.2979 | 0.8609 | 1.8690 | -733.5210 | -535.3586 | 0.6830 | -0.5276 | 0.0943 | 0.9852 | 0.0148 | -1.4038 | -3.4887 | 0.8869 | 2.0849 | -796.4048 | -563.9600 | 0.3914 | -0.7372 | 0.2955 | 0.1278 | -1.4972 | -2.5982 | 0.7618 | 1.1009 | -501.4233 | -438.9818 | 1.8477 | 0.1514 | 0.4804 | 0.0530 |
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| 0.0667 | 1.4990 | 1080 | 0.2667 | -2.1402 | -5.1839 | 0.8656 | 3.0437 | -922.1221 | -606.4852 | 1.0002 | -0.7884 | 0.0408 | 0.8954 | 0.1046 | -2.1729 | -5.7323 | 0.8964 | 3.5594 | -1020.7665 | -640.8754 | 0.8903 | -0.9784 | 0.2150 | 0.0521 | -1.9916 | -3.2293 | 0.7574 | 1.2377 | -564.5363 | -488.4239 | 1.5582 | -0.1961 | 0.4940 | 0.0466 |
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| 0.06 | 1.9986 | 1440 | 0.2626 | -2.1843 | -5.4288 | 0.8684 | 3.2445 | -946.6157 | -610.9032 | 1.2318 | -0.7806 | 0.0374 | 0.8470 | 0.1530 | -2.2281 | -6.0246 | 0.8987 | 3.7965 | -1049.9939 | -646.3860 | 1.1158 | -0.9982 | 0.2102 | 0.0475 | -1.9978 | -3.3091 | 0.7603 | 1.3113 | -572.5212 | -489.0419 | 1.8243 | -0.1004 | 0.4944 | 0.0469 |
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### Framework versions
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- Transformers 4.44.1
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- Pytorch 2.1.2+cu121
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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all_results.json
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{
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"epoch": 1.9986120749479528,
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"total_flos": 0.0,
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"train_loss": 0.15370953861210082,
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"train_runtime": 41916.6173,
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"train_samples": 184443,
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"train_samples_per_second": 8.8,
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"train_steps_per_second": 0.034
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.44.1"
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}
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train_results.json
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{
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"epoch": 1.9986120749479528,
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"total_flos": 0.0,
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"train_loss": 0.15370953861210082,
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"train_runtime": 41916.6173,
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"train_samples": 184443,
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"train_samples_per_second": 8.8,
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"train_steps_per_second": 0.034
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}
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