Instructions to use KaQyn/peft-lora-starcoder2-15b-flutter-copilot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use KaQyn/peft-lora-starcoder2-15b-flutter-copilot with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder2-15b") model = PeftModel.from_pretrained(base_model, "KaQyn/peft-lora-starcoder2-15b-flutter-copilot") - Notebooks
- Google Colab
- Kaggle
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Download README.md from KaQyn/peft-lora-starcoder2-15b-flutter-copilot: direct link, hf CLI and curl.
- Browser
- Download file 2.43 kB
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https://huggingface.co/KaQyn/peft-lora-starcoder2-15b-flutter-copilot/resolve/main/README.md
- Command line
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hf download hf://KaQyn/peft-lora-starcoder2-15b-flutter-copilot/README.md
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curl -L -o README.md https://huggingface.co/KaQyn/peft-lora-starcoder2-15b-flutter-copilot/resolve/main/README.md
2.43 kB
| library_name: peft | |
| tags: | |
| - generated_from_trainer | |
| base_model: bigcode/starcoder2-15b | |
| model-index: | |
| - name: peft-lora-starcoder2-15b-flutter-copilot | |
| 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-lora-starcoder2-15b-flutter-copilot | |
| This model is a fine-tuned version of [bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3895 | |
| ## 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: 4 | |
| - total_train_batch_size: 16 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - training_steps: 2000 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.9837 | 0.05 | 100 | 0.4444 | | |
| | 0.213 | 0.1 | 200 | 0.4486 | | |
| | 0.4039 | 0.15 | 300 | 0.4082 | | |
| | 0.3069 | 0.2 | 400 | 0.4030 | | |
| | 0.3163 | 0.25 | 500 | 0.4044 | | |
| | 0.7574 | 0.3 | 600 | 0.4008 | | |
| | 0.4859 | 0.35 | 700 | 0.3990 | | |
| | 0.5048 | 0.4 | 800 | 0.3984 | | |
| | 0.4226 | 0.45 | 900 | 0.3962 | | |
| | 3.584 | 0.5 | 1000 | 0.3959 | | |
| | 0.624 | 0.55 | 1100 | 0.3958 | | |
| | 0.2663 | 0.6 | 1200 | 0.3954 | | |
| | 0.496 | 0.65 | 1300 | 0.3947 | | |
| | 0.6882 | 0.7 | 1400 | 0.3950 | | |
| | 0.4147 | 0.75 | 1500 | 0.3937 | | |
| | 0.5237 | 0.8 | 1600 | 0.3922 | | |
| | 0.626 | 0.85 | 1700 | 0.3909 | | |
| | 0.6625 | 0.9 | 1800 | 0.3897 | | |
| | 0.6163 | 0.95 | 1900 | 0.3896 | | |
| | 0.5014 | 1.0 | 2000 | 0.3895 | | |
| ### Framework versions | |
| - PEFT 0.9.1.dev0 | |
| - Transformers 4.39.0.dev0 | |
| - Pytorch 2.2.1 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 |