meditron-7b-dpo-full-wo-medication_qa-ep3
This model is a fine-tuned version of epfl-llm/meditron-7b on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:
- Loss: 0.5850
- Rewards/chosen: -0.2075
- Rewards/rejected: -0.5850
- Rewards/accuracies: 0.7881
- Rewards/margins: 0.3775
- Logps/rejected: -1658.2283
- Logps/chosen: -974.4987
- Logits/rejected: -0.8799
- Logits/chosen: -0.7592
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.592 | 0.55 | 100 | 0.6293 | -0.0145 | -0.1627 | 0.7797 | 0.1482 | -1615.9989 | -955.1961 | -0.8722 | -0.7069 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2