Instructions to use HuyTran1301/CodeT5p-ModelPhase2-01072025 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuyTran1301/CodeT5p-ModelPhase2-01072025 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("HuyTran1301/CodeT5p-ModelPhase2-01072025") model = AutoModelForSeq2SeqLM.from_pretrained("HuyTran1301/CodeT5p-ModelPhase2-01072025", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from HuyTran1301/CodeT5p-ModelPhase2-01072025: direct link, hf CLI and curl.
- Browser
- Download file 1.93 kB
-
https://huggingface.co/HuyTran1301/CodeT5p-ModelPhase2-01072025/resolve/main/README.md
- Command line
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hf download hf://HuyTran1301/CodeT5p-ModelPhase2-01072025/README.md
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curl -L -o README.md https://huggingface.co/HuyTran1301/CodeT5p-ModelPhase2-01072025/resolve/main/README.md
1.93 kB
| library_name: transformers | |
| license: bsd-3-clause | |
| base_model: Salesforce/codet5p-220m | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: CodeT5p-ModelPhase2-01072025 | |
| 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. --> | |
| # CodeT5p-ModelPhase2-01072025 | |
| This model is a fine-tuned version of [Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 5.7451 | |
| ## 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: 14 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 10 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 8.2614 | 1.0 | 1 | 10.6939 | | |
| | 8.2039 | 2.0 | 2 | 9.1045 | | |
| | 6.702 | 3.0 | 3 | 7.5605 | | |
| | 5.909 | 4.0 | 4 | 6.4362 | | |
| | 5.2914 | 5.0 | 5 | 6.4362 | | |
| | 5.3337 | 6.0 | 6 | 6.0886 | | |
| | 5.0139 | 7.0 | 7 | 5.9303 | | |
| | 4.9826 | 8.0 | 8 | 5.8531 | | |
| | 4.751 | 9.0 | 9 | 5.7749 | | |
| | 4.6473 | 10.0 | 10 | 5.7451 | | |
| ### Framework versions | |
| - Transformers 4.52.4 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.21.1 | |