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---
library_name: transformers
license: gemma
base_model: google/gemma-2-9b-it
tags:
- alignment-handbook
- trl
- simpo
- generated_from_trainer
- trl
- simpo
- generated_from_trainer
datasets:
- princeton-nlp/gemma2-ultrafeedback-armorm
model-index:
- name: simpo
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. -->
# simpo
This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) on the princeton-nlp/gemma2-ultrafeedback-armorm dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7459
- Rewards/chosen: -18.9664
- Rewards/rejected: -23.9949
- Rewards/accuracies: 0.7725
- Rewards/margins: 5.0285
- Logps/rejected: -2.3995
- Logps/chosen: -1.8966
- Logits/rejected: -14.4878
- Logits/chosen: -14.5537
## 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-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 |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 2.7196 | 0.8594 | 400 | 2.7580 | -18.9526 | -23.9387 | 0.7705 | 4.9861 | -2.3939 | -1.8953 | -14.4321 | -14.5024 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.7.0+cu128
- Datasets 2.18.0
- Tokenizers 0.19.1
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