llama2-7b-dpo-full-wo-kqa_silver_wogold-ep3
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:
- Loss: 0.6588
- Rewards/chosen: 0.0476
- Rewards/rejected: -0.0291
- Rewards/accuracies: 0.7912
- Rewards/margins: 0.0767
- Logps/rejected: -1010.3619
- Logps/chosen: -408.6368
- Logits/rejected: -0.5925
- Logits/chosen: 0.4749
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: 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: 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 | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.5979 | 0.79 | 100 | 0.6597 | 0.0473 | -0.0269 | 0.8044 | 0.0742 | -1010.1399 | -408.6627 | -0.5930 | 0.4742 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
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