metadata
license: mit
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
base_model: microsoft/phi-2
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: phi-2-gpo-test-longest-iter-random2-4
results: []
phi-2-gpo-test-longest-iter-random2-4
This model is a fine-tuned version of DUAL-GPO/phi-2-gpo-test-longest-iter-random2-3 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:
- Loss: 0.0019
- Rewards/chosen: -0.0074
- Rewards/rejected: -0.0063
- Rewards/accuracies: 0.4710
- Rewards/margins: -0.0012
- Logps/rejected: -279.6524
- Logps/chosen: -307.5768
- Logits/rejected: 0.0429
- Logits/chosen: -0.0563
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.001 | 1.6 | 100 | 0.0018 | -0.0035 | -0.0023 | 0.4785 | -0.0012 | -279.2534 | -307.1775 | 0.0583 | -0.0400 |
| 0.0009 | 3.2 | 200 | 0.0019 | -0.0082 | -0.0066 | 0.4565 | -0.0015 | -279.6910 | -307.6504 | 0.0455 | -0.0553 |
Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2