metadata
base_model: meta-llama/Meta-Llama-3-8B
library_name: peft
license: llama3
tags:
- trl
- orpo
- generated_from_trainer
model-index:
- name: ft-Llama3-8b-orpo
results: []
ft-Llama3-8b-orpo
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the mlabonne/orpo-dpo-mix-40k dataset. It achieves the following results on the evaluation set:
- Loss: 0.8983
- Rewards/chosen: -0.0999
- Rewards/rejected: -0.1748
- Rewards/accuracies: 0.4000
- Rewards/margins: 0.0749
- Logps/rejected: -1.7478
- Logps/chosen: -0.9993
- Logits/rejected: -1.5466
- Logits/chosen: -1.5315
- Nll Loss: 0.8281
- Log Odds Ratio: -0.7026
- Log Odds Chosen: 0.7314
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: 8e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- 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 | Nll Loss | Log Odds Ratio | Log Odds Chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.6579 | 0.2 | 25 | 1.2469 | -0.1560 | -0.2318 | 0.5 | 0.0758 | -2.3180 | -1.5595 | -1.2300 | -1.0199 | 1.1776 | -0.6935 | 0.7440 |
| 1.1014 | 0.4 | 50 | 1.0297 | -0.1262 | -0.1994 | 0.5 | 0.0732 | -1.9942 | -1.2621 | -1.4006 | -1.3743 | 0.9587 | -0.7096 | 0.7137 |
| 0.9391 | 0.61 | 75 | 0.9463 | -0.1106 | -0.1844 | 0.5 | 0.0738 | -1.8440 | -1.1062 | -1.5970 | -1.5504 | 0.8754 | -0.7083 | 0.7185 |
| 0.676 | 0.81 | 100 | 0.8983 | -0.0999 | -0.1748 | 0.4000 | 0.0749 | -1.7478 | -0.9993 | -1.5466 | -1.5315 | 0.8281 | -0.7026 | 0.7314 |
Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.4.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2