Regularized-Preference-Optimization
Collection
The models trained in https://github.com/YSLIU627/Regularized-Preference-Optimization
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4 items
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Updated
beta_ultra_rdpo_full_eta0.005_beta0.01_no_decay_new
This model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the updated and the original datasets.
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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- 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 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
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Inference Providers
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Model tree for ZHLiu627/beta_ultra_rdpo_full_eta0.005_beta0.01_no_decay_new
Base model
mistralai/Mistral-7B-v0.1
Finetuned
HuggingFaceH4/mistral-7b-sft-beta