Instructions to use Ruthvik2835/starcoder1b-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ruthvik2835/starcoder1b-finetune with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoderbase-1b") model = PeftModel.from_pretrained(base_model, "Ruthvik2835/starcoder1b-finetune") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Ruthvik2835/starcoder1b-finetune: direct link, hf CLI and curl.
- Browser
- Download file 1.31 kB
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https://huggingface.co/Ruthvik2835/starcoder1b-finetune/resolve/main/README.md
- Command line
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hf download hf://Ruthvik2835/starcoder1b-finetune/README.md
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curl -L -o README.md https://huggingface.co/Ruthvik2835/starcoder1b-finetune/resolve/main/README.md
1.31 kB
| library_name: peft | |
| license: bigcode-openrail-m | |
| base_model: bigcode/starcoderbase-1b | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: starcoder-finetune | |
| 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-finetune | |
| This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown 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: 0.0003 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 16 | |
| - total_train_batch_size: 64 | |
| - 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: cosine | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - training_steps: 75 | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.15.2.dev0 | |
| - Transformers 4.53.0.dev0 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.21.1 |