| --- |
| license: apache-2.0 |
| library_name: peft |
| tags: |
| - trl |
| - sft |
| - generated_from_trainer |
| base_model: ibm-granite/granite-8b-code-instruct |
| datasets: |
| - generator |
| model-index: |
| - name: granite-8b-code-instruct-spider |
| 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. --> |
|
|
| # granite-8b-code-instruct-spider |
|
|
| This model is a fine-tuned version of [ibm-granite/granite-8b-code-instruct](https://huggingface.co/ibm-granite/granite-8b-code-instruct) on the generator dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.8823 |
|
|
| ## 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: 0.0002 |
| - train_batch_size: 1 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 2 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.03 |
| - num_epochs: 1 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:-----:|:----:|:---------------:| |
| | 0.1877 | 0.1 | 100 | 0.6620 | |
| | 0.0784 | 0.2 | 200 | 0.6879 | |
| | 0.0552 | 0.3 | 300 | 0.7506 | |
| | 0.0607 | 0.4 | 400 | 0.8319 | |
| | 0.049 | 0.51 | 500 | 0.8430 | |
| | 0.0485 | 0.61 | 600 | 0.8774 | |
| | 0.0448 | 0.71 | 700 | 0.8839 | |
| | 0.0478 | 0.81 | 800 | 0.8807 | |
| | 0.0472 | 0.91 | 900 | 0.8823 | |
|
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|
|
| ### Framework versions |
|
|
| - PEFT 0.7.2.dev0 |
| - Transformers 4.37.0 |
| - Pytorch 2.1.2+cu121 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.2 |
