This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the princeton-nlp/llama3-ultrafeedback dataset. It achieves the following results on the evaluation set:
- Loss: 1.2416
- Rewards/chosen: -0.3361
- Rewards/rejected: -0.4013
- Rewards/accuracies: 0.5915
- Rewards/margins: 0.0652
- Logps/rejected: -0.4013
- Logps/chosen: -0.3361
- Logits/rejected: 0.0031
- Logits/chosen: 0.0123
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: 6e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- 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 | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.2443 | 0.8550 | 400 | 1.2416 | -0.3361 | -0.4013 | 0.5915 | 0.0652 | -0.4013 | -0.3361 | 0.0031 | 0.0123 |
Framework versions
- Transformers 4.42.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
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meta-llama/Meta-Llama-3-8B-Instruct