Instructions to use Toastmachine/Orsay_museum_test_ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Toastmachine/Orsay_museum_test_ko with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("saltlux/Ko-Llama3-Luxia-8B") model = PeftModel.from_pretrained(base_model, "Toastmachine/Orsay_museum_test_ko") - Notebooks
- Google Colab
- Kaggle
File size: 1,255 Bytes
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license: llama3
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: saltlux/Ko-Llama3-Luxia-8B
model-index:
- name: Orsay_museum_test_ko
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Orsay_museum_test_ko
This model is a fine-tuned version of [saltlux/Ko-Llama3-Luxia-8B](https://huggingface.co/saltlux/Ko-Llama3-Luxia-8B) on the Orsay_meseum_exhibited 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |