Instructions to use Luongdzung/hoa-1b4-finetune-cl7-m-l-h-g-b-p-che-small-VN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Luongdzung/hoa-1b4-finetune-cl7-m-l-h-g-b-p-che-small-VN with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Luongdzung/hoa-1b4-finetune-cl6-m-l-h-g-b-phy-small-VN-ALL-WEIGHT") model = PeftModel.from_pretrained(base_model, "Luongdzung/hoa-1b4-finetune-cl7-m-l-h-g-b-p-che-small-VN") - Notebooks
- Google Colab
- Kaggle
hoa-1b4-finetune-cl7-m-l-h-g-b-p-che-small-VN
This model is a fine-tuned version of Luongdzung/hoa-1b4-finetune-cl6-m-l-h-g-b-phy-small-VN-ALL-WEIGHT on an unknown 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
Training results
Framework versions
- PEFT 0.14.0
- Transformers 4.48.0
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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