gemma-2-9b-it-DPO / README.md
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---
base_model: /scratch/gpfs/DANQIC/ym0081/hf_cache/gemma-2-9b-it
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- /scratch/gpfs/DANQIC/ym0081/hf_cache/gemma2-ultrafeedback-armorm/dataset_dict/
model-index:
- name: gemma2-9b-dpo-beta-0.01-lr-5e-7
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. -->
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# gemma2-9b-dpo-beta-0.01-lr-5e-7
This model is a fine-tuned version of [/scratch/gpfs/DANQIC/ym0081/hf_cache/gemma-2-9b-it](https://huggingface.co//scratch/gpfs/DANQIC/ym0081/hf_cache/gemma-2-9b-it) on the /scratch/gpfs/DANQIC/ym0081/hf_cache/gemma2-ultrafeedback-armorm/dataset_dict/ dataset.
## 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: 5e-07
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- 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
### Framework versions
- Transformers 4.42.4
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1