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---
library_name: peft
license: bigcode-openrail-m
base_model: bigcode/starcoderbase-1b
tags:
- generated_from_trainer
model-index:
- name: EMS_all_100_code-starcoder-lora-batch_10_steps_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_all_100_code-starcoder-lora-batch_10_steps_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: 0.0375
## 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: 10
- eval_batch_size: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.416 | 0.05 | 100 | 0.4470 |
| 0.3877 | 0.1 | 200 | 0.3435 |
| 0.3784 | 0.15 | 300 | 0.2490 |
| 0.1561 | 0.2 | 400 | 0.2118 |
| 0.2157 | 0.25 | 500 | 0.1682 |
| 0.1654 | 0.3 | 600 | 0.1379 |
| 0.1178 | 0.35 | 700 | 0.1232 |
| 0.1623 | 0.4 | 800 | 0.1032 |
| 0.1204 | 0.45 | 900 | 0.0854 |
| 0.1155 | 0.5 | 1000 | 0.0702 |
| 0.0812 | 0.55 | 1100 | 0.0633 |
| 0.0993 | 0.6 | 1200 | 0.0550 |
| 0.0704 | 0.65 | 1300 | 0.0496 |
| 0.0816 | 0.7 | 1400 | 0.0456 |
| 0.0767 | 0.75 | 1500 | 0.0421 |
| 0.0658 | 0.8 | 1600 | 0.0397 |
| 0.0612 | 0.85 | 1700 | 0.0385 |
| 0.0557 | 0.9 | 1800 | 0.0379 |
| 0.07 | 0.95 | 1900 | 0.0376 |
| 0.0695 | 1.0 | 2000 | 0.0375 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.20.3 |