stablelm-2-1_6b-orpo-full-v1
This model is a fine-tuned version of stabilityai/stablelm-2-1_6b on the alvarobartt/dpo-mix-7k-simplified dataset. It achieves the following results on the evaluation set:
- Loss: 1.3790
- Rewards/chosen: -0.1279
- Rewards/rejected: -0.1387
- Rewards/accuracies: 0.5625
- Rewards/margins: 0.0114
- Logps/rejected: -1.3906
- Logps/chosen: -1.2734
- Logits/rejected: -2.4688
- Logits/chosen: -2.4844
- Nll Loss: 1.3359
- Log Odds Ratio: -0.6799
- Log Odds Chosen: 0.1751
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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- total_eval_batch_size: 24
- 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 | Nll Loss | Log Odds Ratio | Log Odds Chosen |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1.4572 | 0.71 | 100 | 1.4374 | -0.1406 | -0.1514 | 0.5469 | 0.0109 | -1.5078 | -1.4062 | -2.3594 | -2.375 | 1.3906 | -0.6874 | 0.1528 |
1.3846 | 1.42 | 200 | 1.3829 | -0.1279 | -0.1396 | 0.5625 | 0.0115 | -1.3984 | -1.2812 | -2.4688 | -2.4844 | 1.3359 | -0.6793 | 0.1746 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
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Base model
stabilityai/stablelm-2-1_6b