Instructions to use cy948/starcoder-peft-airscript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cy948/starcoder-peft-airscript with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoderbase-1b") model = PeftModel.from_pretrained(base_model, "cy948/starcoder-peft-airscript") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| license: bigcode-openrail-m | |
| base_model: bigcode/starcoderbase-1b | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: starcoder-peft-airscript | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # starcoder-peft-airscript | |
| This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.7248 | |
| ## 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: 0.0005 | |
| - train_batch_size: 10 | |
| - eval_batch_size: 10 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 20 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 30 | |
| - training_steps: 1600 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 1.2597 | 0.0625 | 100 | 1.1604 | | |
| | 0.9591 | 0.125 | 200 | 0.9402 | | |
| | 0.8109 | 0.1875 | 300 | 0.8431 | | |
| | 0.7151 | 0.25 | 400 | 0.7917 | | |
| | 0.6362 | 0.3125 | 500 | 0.7607 | | |
| | 0.5759 | 0.375 | 600 | 0.7401 | | |
| | 0.5284 | 0.4375 | 700 | 0.7334 | | |
| | 0.4926 | 0.5 | 800 | 0.7252 | | |
| | 0.4616 | 0.5625 | 900 | 0.7212 | | |
| | 0.4369 | 0.625 | 1000 | 0.7236 | | |
| | 0.4111 | 0.6875 | 1100 | 0.7255 | | |
| | 0.3969 | 0.75 | 1200 | 0.7236 | | |
| | 0.3855 | 0.8125 | 1300 | 0.7260 | | |
| | 0.3822 | 0.875 | 1400 | 0.7262 | | |
| | 0.3768 | 0.9375 | 1500 | 0.7256 | | |
| | 0.3778 | 1.0 | 1600 | 0.7248 | | |
| ### Framework versions | |
| - PEFT 0.13.2 | |
| - Transformers 4.45.2 | |
| - Pytorch 2.5.0 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.20.1 |