Description
This model was trained as part of the Reinforcement Learning - 24 project at Peking University, focusing on [simpo].
Authors
- Ejafa Bassam
- Yaroslav Ponomarenko
phi-3-mini-128k-instruct-simpo-lr-5e-07-gamma-1.5
This model is a fine-tuned version of microsoft/Phi-3-mini-128k-instruct on the princeton-nlp/llama3-ultrafeedback dataset. It achieves the following results on the evaluation set:
- Loss: 1.6226
- Rewards/chosen: -2.2430
- Rewards/rejected: -2.6527
- Rewards/accuracies: 0.625
- Rewards/margins: 0.4097
- Logps/rejected: -1.0611
- Logps/chosen: -0.8972
- Logits/rejected: 2.0148
- Logits/chosen: 2.0096
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: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- 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: 0.1
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.6417 | 0.8549 | 400 | 1.6236 | -2.2390 | -2.6457 | 0.6210 | 0.4067 | -1.0583 | -0.8956 | 2.0190 | 2.0146 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1