zephyr-7b-sft-full-orpo
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:
- Loss: 0.4701
- Rewards/chosen: -0.0364
- Rewards/rejected: -0.0499
- Rewards/accuracies: 0.6587
- Rewards/margins: 0.0135
- Logps/rejected: -0.9978
- Logps/chosen: -0.7282
- Logits/rejected: -2.9263
- Logits/chosen: -2.9434
- Nll Loss: 0.4357
- Log Odds Ratio: -0.6093
- Log Odds Chosen: 0.4456
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: 7e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_steps: 100
- 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 | Nll Loss | Log Odds Ratio | Log Odds Chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.5226 | 0.1049 | 100 | 0.5280 | -0.0386 | -0.0472 | 0.6329 | 0.0086 | -0.9448 | -0.7728 | -2.7583 | -2.7860 | 0.4953 | -0.6326 | 0.2873 |
| 0.5074 | 0.2098 | 200 | 0.5134 | -0.0381 | -0.0478 | 0.6409 | 0.0098 | -0.9566 | -0.7612 | -2.6736 | -2.7002 | 0.4774 | -0.6357 | 0.3190 |
| 0.5265 | 0.3146 | 300 | 0.5012 | -0.0379 | -0.0479 | 0.6329 | 0.0099 | -0.9572 | -0.7588 | -2.7317 | -2.7594 | 0.4653 | -0.6374 | 0.3278 |
| 0.5194 | 0.4195 | 400 | 0.4912 | -0.0371 | -0.0478 | 0.6429 | 0.0107 | -0.9559 | -0.7417 | -2.6640 | -2.6974 | 0.4560 | -0.6284 | 0.3607 |
| 0.5008 | 0.5244 | 500 | 0.4847 | -0.0373 | -0.0489 | 0.6508 | 0.0117 | -0.9786 | -0.7455 | -2.5957 | -2.6294 | 0.4499 | -0.6209 | 0.3873 |
| 0.4725 | 0.6293 | 600 | 0.4794 | -0.0362 | -0.0470 | 0.6349 | 0.0107 | -0.9394 | -0.7248 | -2.6147 | -2.6477 | 0.4435 | -0.6320 | 0.3567 |
| 0.4875 | 0.7341 | 700 | 0.4767 | -0.0368 | -0.0498 | 0.6409 | 0.0129 | -0.9955 | -0.7365 | -2.6910 | -2.7213 | 0.4416 | -0.6158 | 0.4180 |
| 0.4796 | 0.8390 | 800 | 0.4740 | -0.0371 | -0.0508 | 0.6508 | 0.0137 | -1.0162 | -0.7416 | -2.7913 | -2.8114 | 0.4396 | -0.6169 | 0.4363 |
| 0.4851 | 0.9439 | 900 | 0.4714 | -0.0357 | -0.0466 | 0.6528 | 0.0109 | -0.9324 | -0.7143 | -2.9543 | -2.9692 | 0.4361 | -0.6245 | 0.3669 |
Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1