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base_model: NousResearch/Meta-Llama-3-8B-Instruct
library_name: peft
license: other
tags:
- trl
- orpo
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1433
- Rewards/chosen: -0.0156
- Rewards/rejected: -1.1268
- Rewards/accuracies: 1.0
- Rewards/margins: 1.1112
- Logps/rejected: -11.2681
- Logps/chosen: -0.1556
- Logits/rejected: -0.2138
- Logits/chosen: 0.6631
- Nll Loss: 0.1433
- Log Odds Ratio: -0.0000
- Log Odds Chosen: 13.2065
## 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-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|:--------:|:--------------:|:---------------:|
| 0.1869 | 0.5001 | 2175 | 0.1433 | -0.0156 | -1.1268 | 1.0 | 1.1112 | -11.2681 | -0.1556 | -0.2138 | 0.6631 | 0.1433 | -0.0000 | 13.2065 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |