Instructions to use kyuhyun/dpo_trainer_ver2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kyuhyun/dpo_trainer_ver2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("yanolja/EEVE-Korean-Instruct-10.8B-v1.0") model = PeftModel.from_pretrained(base_model, "kyuhyun/dpo_trainer_ver2") - Notebooks
- Google Colab
- Kaggle
dpo_trainer_ver2
This model is a fine-tuned version of yanolja/EEVE-Korean-Instruct-10.8B-v1.0 on the None 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
Training results
Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.0
- Pytorch 2.2.2
- Datasets 2.19.0
- Tokenizers 0.19.1
- Downloads last month
- 1
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for kyuhyun/dpo_trainer_ver2
Base model
upstage/SOLAR-10.7B-v1.0 Finetuned
yanolja/YanoljaNEXT-EEVE-10.8B Finetuned
yanolja/YanoljaNEXT-EEVE-Instruct-10.8B