Instructions to use KhaiQuang/ApecGPT-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use KhaiQuang/ApecGPT-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("vinai/PhoGPT-4B-Chat") model = PeftModel.from_pretrained(base_model, "KhaiQuang/ApecGPT-LoRA") - Notebooks
- Google Colab
- Kaggle
ApecGPT-LoRA
This model is a fine-tuned version of vinai/PhoGPT-4B-Chat 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1200
Training results
Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
- Downloads last month
- 7
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for KhaiQuang/ApecGPT-LoRA
Base model
vinai/PhoGPT-4B-Chat