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phi-2-apo
rasyosef/phi-2-apo
phi-2-apo is a machine learning model from rasyosef. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- Access
- Public
- Created
- Sep 15, 2024
- Updated
- Sep 16, 2024
- SHA
- 4a25c7a7
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
phi-2-apo
This model is a fine-tuned version of rasyosef/phi-2-sft-openhermes-128k-v2-merged on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3695
- Rewards/chosen: 1.5931
- Rewards/rejected: -3.0842
- Rewards/accuracies: 0.9350
- Rewards/margins: 4.6772
- Logps/rejected: -173.1941
- Logps/chosen: -253.6105
- Logits/rejected: -0.4322
- Logits/chosen: 0.1424
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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.3669 | 0.2041 | 250 | 0.3828 | 1.5010 | -2.9712 | 0.9450 | 4.4722 | -172.0644 | -254.5310 | -0.4930 | 0.0860 |
| 0.3514 | 0.4082 | 500 | 0.3786 | 1.5375 | -2.9788 | 0.9400 | 4.5163 | -172.1404 | -254.1665 | -0.4834 | 0.0968 |
| 0.3539 | 0.6122 | 750 | 0.3756 | 1.5549 | -3.0097 | 0.9400 | 4.5647 | -172.4500 | -253.9920 | -0.4690 | 0.1096 |
| 0.3562 | 0.8163 | 1000 | 0.3736 | 1.5759 | -3.0081 | 0.9450 | 4.5840 | -172.4332 | -253.7824 | -0.4558 | 0.1220 |
| 0.3437 | 1.0204 | 1250 | 0.3720 | 1.5665 | -3.0805 | 0.9350 | 4.6470 | -173.1577 | -253.8766 | -0.4445 | 0.1325 |
| 0.3503 | 1.2245 | 1500 | 0.3710 | 1.5889 | -3.0515 | 0.9400 | 4.6404 | -172.8680 | -253.6525 | -0.4406 | 0.1347 |
| 0.3427 | 1.4286 | 1750 | 0.3697 | 1.5903 | -3.0719 | 0.9450 | 4.6622 | -173.0719 | -253.6384 | -0.4355 | 0.1387 |
| 0.3353 | 1.6327 | 2000 | 0.3699 | 1.5881 | -3.0875 | 0.9400 | 4.6756 | -173.2272 | -253.6602 | -0.4333 | 0.1412 |
| 0.3441 | 1.8367 | 2250 | 0.3695 | 1.5931 | -3.0842 | 0.9350 | 4.6772 | -173.1941 | -253.6105 | -0.4322 | 0.1424 |
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
- Library
- peft
- Created
- Sep 15, 2024
- Updated
- Sep 16, 2024