Instructions to use rahkrish1/EMS_tb_code-starcoder-lora-batch_5_2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rahkrish1/EMS_tb_code-starcoder-lora-batch_5_2000 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoderbase-1b") model = PeftModel.from_pretrained(base_model, "rahkrish1/EMS_tb_code-starcoder-lora-batch_5_2000") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| license: bigcode-openrail-m | |
| base_model: bigcode/starcoderbase-1b | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: EMS_tb_code-starcoder-lora-batch_5_2000 | |
| 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. --> | |
| # EMS_tb_code-starcoder-lora-batch_5_2000 | |
| 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: 0.0078 | |
| ## 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: 5 | |
| - eval_batch_size: 5 | |
| - 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: cosine | |
| - lr_scheduler_warmup_steps: 30 | |
| - training_steps: 2000 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.3234 | 0.05 | 100 | 0.1921 | | |
| | 0.082 | 0.1 | 200 | 0.0435 | | |
| | 0.0455 | 0.15 | 300 | 0.0258 | | |
| | 0.0334 | 0.2 | 400 | 0.0198 | | |
| | 0.0297 | 0.25 | 500 | 0.0171 | | |
| | 0.025 | 0.3 | 600 | 0.0149 | | |
| | 0.0223 | 0.35 | 700 | 0.0134 | | |
| | 0.0211 | 0.4 | 800 | 0.0125 | | |
| | 0.0158 | 0.45 | 900 | 0.0116 | | |
| | 0.0155 | 0.5 | 1000 | 0.0107 | | |
| | 0.0137 | 0.55 | 1100 | 0.0101 | | |
| | 0.0129 | 0.6 | 1200 | 0.0095 | | |
| | 0.0143 | 0.65 | 1300 | 0.0092 | | |
| | 0.0132 | 0.7 | 1400 | 0.0087 | | |
| | 0.0116 | 0.75 | 1500 | 0.0083 | | |
| | 0.0124 | 0.8 | 1600 | 0.0081 | | |
| | 0.0126 | 0.85 | 1700 | 0.0079 | | |
| | 0.0112 | 0.9 | 1800 | 0.0078 | | |
| | 0.0111 | 0.95 | 1900 | 0.0078 | | |
| | 0.0114 | 1.0 | 2000 | 0.0078 | | |
| ### Framework versions | |
| - PEFT 0.14.0 | |
| - Transformers 4.46.3 | |
| - Pytorch 2.5.1+cu124 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.20.3 |