phi3-offline-dpo-lora-noise-0.0-5e-7-42
This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6914
- Rewards/chosen: -0.0098
- Rewards/rejected: -0.0147
- Rewards/accuracies: 0.5913
- Rewards/margins: 0.0049
- Logps/rejected: -385.2070
- Logps/chosen: -409.5497
- Logits/rejected: 12.4993
- Logits/chosen: 14.2906
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-07
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_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: 1
Training results
Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
---|---|---|---|---|---|---|---|---|---|---|---|
0.6933 | 0.1778 | 100 | 0.6932 | 0.0002 | -0.0009 | 0.5278 | 0.0011 | -383.8265 | -408.5499 | 12.5336 | 14.3223 |
0.6942 | 0.3556 | 200 | 0.6928 | -0.0035 | -0.0064 | 0.6111 | 0.0029 | -384.3740 | -408.9154 | 12.5221 | 14.3092 |
0.6933 | 0.5333 | 300 | 0.6918 | -0.0073 | -0.0121 | 0.5794 | 0.0048 | -384.9481 | -409.3039 | 12.5135 | 14.3020 |
0.6928 | 0.7111 | 400 | 0.6914 | -0.0104 | -0.0142 | 0.5714 | 0.0038 | -385.1545 | -409.6051 | 12.4934 | 14.2847 |
0.6935 | 0.8889 | 500 | 0.6912 | -0.0101 | -0.0159 | 0.5913 | 0.0058 | -385.3226 | -409.5751 | 12.4995 | 14.2891 |
Framework versions
- PEFT 0.7.1
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
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Model tree for Wenboz/phi3-offline-dpo-lora-noise-0.0-5e-7-42
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microsoft/Phi-3-mini-4k-instruct
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this model