llama3_qfUNL_best_entropy
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the yakazimir/llama3-ultrafeedback-armorm dataset. It achieves the following results on the evaluation set:
- Loss: 2.0906
- Rewards/chosen: -5.3136
- Rewards/rejected: -7.1413
- Rewards/accuracies: 0.7741
- Rewards/margins: 1.8277
- Logps/rejected: -0.7141
- Logps/chosen: -0.5314
- Logits/rejected: -1.3346
- Logits/chosen: -1.3749
- Semantic Entropy: 0.9976
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: 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: 1.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | Semantic Entropy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.0424 | 0.8743 | 400 | 2.0936 | -5.3144 | -7.1419 | 0.7771 | 1.8275 | -0.7142 | -0.5314 | -1.3326 | -1.3731 | 0.9976 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
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Base model
meta-llama/Meta-Llama-3-8B-Instruct