phi-2-gpo-test-longest-iter-3
This model is a fine-tuned version of DUAL-GPO/phi-2-gpo-test-longest-iter-2 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:
- Loss: 0.0105
- Rewards/chosen: 0.0036
- Rewards/rejected: 0.0021
- Rewards/accuracies: 0.5235
- Rewards/margins: 0.0015
- Logps/rejected: -278.5249
- Logps/chosen: -306.1786
- Logits/rejected: 0.0859
- Logits/chosen: -0.0119
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: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0102 | 1.6 | 100 | 0.0108 | 0.0012 | 0.0014 | 0.4830 | -0.0002 | -278.6008 | -306.4147 | 0.0907 | -0.0070 |
Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
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Base model
microsoft/phi-2