Instructions to use jlpan/starcoder-c2py-testprogram with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jlpan/starcoder-c2py-testprogram with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder") model = PeftModel.from_pretrained(base_model, "jlpan/starcoder-c2py-testprogram") - Notebooks
- Google Colab
- Kaggle
| license: bigcode-openrail-m | |
| base_model: bigcode/starcoder | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: starcoder-c2py-testprogram | |
| results: [] | |
| library_name: peft | |
| <!-- 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-c2py-testprogram | |
| This model is a fine-tuned version of [bigcode/starcoder](https://huggingface.co/bigcode/starcoder) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1295 | |
| ## 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-05 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 10 | |
| - training_steps: 100 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.1742 | 0.05 | 5 | 0.1665 | | |
| | 0.1587 | 0.1 | 10 | 0.1619 | | |
| | 0.1361 | 0.15 | 15 | 0.1568 | | |
| | 0.1516 | 0.2 | 20 | 0.1486 | | |
| | 0.141 | 0.25 | 25 | 0.1429 | | |
| | 0.1292 | 0.3 | 30 | 0.1405 | | |
| | 0.1461 | 0.35 | 35 | 0.1374 | | |
| | 0.09 | 0.4 | 40 | 0.1351 | | |
| | 0.1149 | 0.45 | 45 | 0.1332 | | |
| | 0.1296 | 0.5 | 50 | 0.1325 | | |
| | 0.1069 | 1.05 | 55 | 0.1334 | | |
| | 0.1046 | 1.1 | 60 | 0.1320 | | |
| | 0.0969 | 1.15 | 65 | 0.1308 | | |
| | 0.1141 | 1.2 | 70 | 0.1304 | | |
| | 0.1182 | 1.25 | 75 | 0.1300 | | |
| | 0.1112 | 1.3 | 80 | 0.1297 | | |
| | 0.1278 | 1.35 | 85 | 0.1295 | | |
| | 0.0794 | 1.4 | 90 | 0.1295 | | |
| | 0.1067 | 1.45 | 95 | 0.1295 | | |
| | 0.1164 | 1.5 | 100 | 0.1295 | | |
| ### Framework versions | |
| - PEFT 0.5.0.dev0 | |
| - PEFT 0.5.0.dev0 | |
| - PEFT 0.5.0.dev0 | |
| - PEFT 0.5.0.dev0 | |
| - PEFT 0.5.0.dev0 | |
| - PEFT 0.5.0.dev0 | |
| - PEFT 0.5.0.dev0 | |
| - PEFT 0.5.0.dev0 | |
| - Transformers 4.32.0.dev0 | |
| - Pytorch 2.0.1+cu117 | |
| - Datasets 2.12.0 | |
| - Tokenizers 0.13.3 | |