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--- |
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license: mit |
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library_name: peft |
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tags: |
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- alignment-handbook |
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- generated_from_trainer |
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- trl |
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- dpo |
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- generated_from_trainer |
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base_model: microsoft/phi-2 |
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datasets: |
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- HuggingFaceH4/ultrafeedback_binarized |
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model-index: |
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- name: phi-2-gpo-test-longest-iter-random1-1 |
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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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# phi-2-gpo-test-longest-iter-random1-1 |
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This model is a fine-tuned version of [DUAL-GPO/phi-2-gpo-test-longest-iter-random1-0](https://huggingface.co/DUAL-GPO/phi-2-gpo-test-longest-iter-random1-0) on the HuggingFaceH4/ultrafeedback_binarized dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0012 |
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- Rewards/chosen: -0.0006 |
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- Rewards/rejected: -0.0003 |
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- Rewards/accuracies: 0.4780 |
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- Rewards/margins: -0.0003 |
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- Logps/rejected: -278.6935 |
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- Logps/chosen: -306.4963 |
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- Logits/rejected: 0.0865 |
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- Logits/chosen: -0.0098 |
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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-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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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: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 4 |
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### Training results |
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### Framework versions |
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- PEFT 0.7.1 |
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- Transformers 4.36.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.15.2 |
