Instructions to use zulqarnain-kernel/peft-starcoder-lora-a100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use zulqarnain-kernel/peft-starcoder-lora-a100 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoderbase-1b") model = PeftModel.from_pretrained(base_model, "zulqarnain-kernel/peft-starcoder-lora-a100") - Notebooks
- Google Colab
- Kaggle
| license: bigcode-openrail-m | |
| library_name: peft | |
| tags: | |
| - generated_from_trainer | |
| base_model: bigcode/starcoderbase-1b | |
| model-index: | |
| - name: peft-starcoder-lora-a100 | |
| 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. --> | |
| # peft-starcoder-lora-a100 | |
| 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: 1.0589 | |
| ## 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: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 30 | |
| - training_steps: 2000 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.9659 | 0.05 | 100 | 0.9183 | | |
| | 0.9898 | 0.1 | 200 | 0.9449 | | |
| | 0.6517 | 0.15 | 300 | 0.9680 | | |
| | 0.8964 | 0.2 | 400 | 0.9818 | | |
| | 0.9497 | 0.25 | 500 | 0.9720 | | |
| | 0.5741 | 0.3 | 600 | 1.0102 | | |
| | 0.7987 | 0.35 | 700 | 1.0202 | | |
| | 0.8268 | 0.4 | 800 | 1.0128 | | |
| | 0.5202 | 0.45 | 900 | 1.0189 | | |
| | 0.7509 | 0.5 | 1000 | 1.0335 | | |
| | 0.772 | 0.55 | 1100 | 1.0386 | | |
| | 0.4747 | 0.6 | 1200 | 1.0525 | | |
| | 0.696 | 0.65 | 1300 | 1.0382 | | |
| | 0.684 | 0.7 | 1400 | 1.0469 | | |
| | 0.5396 | 0.75 | 1500 | 1.0523 | | |
| | 0.6567 | 0.8 | 1600 | 1.0546 | | |
| | 0.6006 | 0.85 | 1700 | 1.0596 | | |
| | 0.6014 | 0.9 | 1800 | 1.0620 | | |
| | 0.5551 | 0.95 | 1900 | 1.0612 | | |
| | 0.5881 | 1.0 | 2000 | 1.0589 | | |
| ### Framework versions | |
| - PEFT 0.11.1 | |
| - Transformers 4.41.2 | |
| - Pytorch 2.3.0+cu121 | |
| - Datasets 2.19.2 | |
| - Tokenizers 0.19.1 |