llama-3.2-3b-dpo
This model is a fine-tuned version of tanliboy/llama-3.2-3b-sft on the HuggingFaceH4/orca_dpo_pairs and the HuggingFaceH4/ultrafeedback_binarized datasets. It achieves the following results on the evaluation set:
- Loss: 0.6289
- Rewards/chosen: 0.7479
- Rewards/rejected: -3.8379
- Rewards/accuracies: 0.7405
- Rewards/margins: 4.5857
- Logps/rejected: -370.2327
- Logps/chosen: -338.3392
- Logits/rejected: 0.4475
- Logits/chosen: 0.3731
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: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
---|---|---|---|---|---|---|---|---|---|---|---|
0.5801 | 0.4739 | 100 | 0.6840 | 0.6485 | -2.9389 | 0.6899 | 3.5875 | -361.2435 | -339.3325 | 0.6783 | 0.6103 |
0.537 | 0.9479 | 200 | 0.6514 | 0.2045 | -4.0315 | 0.7278 | 4.2360 | -372.1696 | -343.7731 | 0.5648 | 0.4948 |
0.4787 | 1.4218 | 300 | 0.6387 | 0.4099 | -3.9882 | 0.7215 | 4.3981 | -371.7361 | -341.7187 | 0.5326 | 0.4589 |
0.4559 | 1.8957 | 400 | 0.6332 | 0.7690 | -3.6688 | 0.7342 | 4.4379 | -368.5425 | -338.1277 | 0.4841 | 0.4110 |
0.4028 | 2.3697 | 500 | 0.6289 | 0.7479 | -3.8379 | 0.7405 | 4.5857 | -370.2327 | -338.3392 | 0.4475 | 0.3731 |
0.4029 | 2.8436 | 600 | 0.6284 | 0.8504 | -3.7058 | 0.7437 | 4.5562 | -368.9125 | -337.3143 | 0.4571 | 0.3820 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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