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---
license: other
base_model: apple/OpenELM-270M
tags:
- trl
- orpo
- generated_from_trainer
model-index:
- name: ft-openelm-270m-ultrafeedback
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. -->
# ft-openelm-270m-ultrafeedback
This model is a fine-tuned version of [apple/OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6455
- Rewards/chosen: -0.1995
- Rewards/rejected: -0.2029
- Rewards/accuracies: 0.5050
- Rewards/margins: 0.0035
- Logps/rejected: -2.0293
- Logps/chosen: -1.9941
- Logits/rejected: -5.7383
- Logits/chosen: -6.1055
- Nll Loss: 1.5752
- Log Odds Ratio: -0.7037
- Log Odds Chosen: 0.0445
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- 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 | Nll Loss | Log Odds Ratio | Log Odds Chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|:--------:|:--------------:|:---------------:|
| 1.7595 | 0.53 | 100 | 1.6455 | -0.1995 | -0.2029 | 0.5050 | 0.0035 | -2.0293 | -1.9941 | -5.7383 | -6.1055 | 1.5752 | -0.7037 | 0.0445 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|