Quantization made by Richard Erkhov.
dpo-selective-buffer-spo-shift - GGUF
Original model description:
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: dpo-selective-buffer-spo-shift
results: []
dpo-selective-buffer-spo-shift
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6777
- Rewards/chosen: -0.1371
- Rewards/rejected: -0.0830
- Rewards/accuracies: 0.4693
- Rewards/margins: -0.0541
- Rewards/safe Rewards: -0.1332
- Rewards/unsafe Rewards: -0.1263
- Logps/rejected: -92.4348
- Logps/chosen: -131.0029
- Logits/rejected: -1.8308
- Logits/chosen: -2.0825
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: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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 | Rewards/safe Rewards | Rewards/unsafe Rewards | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|
| 131.6857 | 0.27 | 500 | 0.8894 | -0.1023 | -0.0129 | 0.4546 | -0.0893 | -0.1043 | -0.1017 | -92.3648 | -130.9681 | -1.8032 | -2.0565 |
| 34.7958 | 0.54 | 1000 | 0.7397 | -0.1263 | -0.1290 | 0.5028 | 0.0026 | -0.1237 | -0.1264 | -92.4809 | -130.9922 | -1.7990 | -2.0551 |
| 15.9924 | 0.81 | 1500 | 0.6823 | -0.1578 | -0.1077 | 0.4713 | -0.0501 | -0.1557 | -0.1535 | -92.4596 | -131.0237 | -1.8335 | -2.0849 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
