Instructions to use jlpan/starcoder-tune-cpp2py-program1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jlpan/starcoder-tune-cpp2py-program1 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-tune-cpp2py-program1") - Notebooks
- Google Colab
- Kaggle
| license: bigcode-openrail-m | |
| base_model: bigcode/starcoder | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: starcoder-tune-cpp2py-program1 | |
| 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-tune-cpp2py-program1 | |
| 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: 0.2677 | |
| ## 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: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 16 | |
| - total_train_batch_size: 16 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 50 | |
| - training_steps: 500 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.3164 | 0.1 | 50 | 0.2789 | | |
| | 0.299 | 0.2 | 100 | 0.2729 | | |
| | 0.3001 | 0.3 | 150 | 0.2711 | | |
| | 0.2902 | 0.4 | 200 | 0.2699 | | |
| | 0.2892 | 0.5 | 250 | 0.2692 | | |
| | 0.2936 | 0.6 | 300 | 0.2686 | | |
| | 0.2872 | 0.7 | 350 | 0.2680 | | |
| | 0.2872 | 0.8 | 400 | 0.2678 | | |
| | 0.2913 | 0.9 | 450 | 0.2677 | | |
| | 0.2848 | 1.0 | 500 | 0.2677 | | |
| ### 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 | |
| - 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 | |