Instructions to use alphablyat/out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use alphablyat/out with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoderbase-1b") model = PeftModel.from_pretrained(base_model, "alphablyat/out") - Notebooks
- Google Colab
- Kaggle
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Download README.md from alphablyat/out: direct link, hf CLI and curl.
- Browser
- Download file 2.33 kB
-
https://huggingface.co/alphablyat/out/resolve/main/README.md
- Command line
-
hf download hf://alphablyat/out/README.md
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curl -L -o README.md https://huggingface.co/alphablyat/out/resolve/main/README.md
2.33 kB
| base_model: bigcode/starcoderbase-1b | |
| library_name: peft | |
| license: bigcode-openrail-m | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: out | |
| 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. --> | |
| # out | |
| 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: 0.7275 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.849 | 0.05 | 100 | 0.7259 | | |
| | 0.777 | 0.1 | 200 | 0.7328 | | |
| | 0.71 | 0.15 | 300 | 0.7398 | | |
| | 0.7214 | 0.2 | 400 | 0.7538 | | |
| | 0.6868 | 0.25 | 500 | 0.7621 | | |
| | 0.8951 | 0.3 | 600 | 0.7636 | | |
| | 0.9411 | 0.35 | 700 | 0.7690 | | |
| | 0.9354 | 0.4 | 800 | 0.7763 | | |
| | 0.8658 | 0.45 | 900 | 0.7617 | | |
| | 0.7322 | 0.5 | 1000 | 0.7428 | | |
| | 0.8219 | 0.55 | 1100 | 0.7444 | | |
| | 0.8431 | 0.6 | 1200 | 0.7444 | | |
| | 0.8644 | 0.65 | 1300 | 0.7459 | | |
| | 0.8033 | 0.7 | 1400 | 0.7449 | | |
| | 0.77 | 0.75 | 1500 | 0.7398 | | |
| | 0.3502 | 0.8 | 1600 | 0.7405 | | |
| | 0.5107 | 0.85 | 1700 | 0.7308 | | |
| | 0.8587 | 0.9 | 1800 | 0.7265 | | |
| | 0.714 | 0.95 | 1900 | 0.7277 | | |
| | 0.7406 | 1.0 | 2000 | 0.7275 | | |
| ### Framework versions | |
| - PEFT 0.11.1 | |
| - Transformers 4.42.4 | |
| - Pytorch 2.3.1+cu121 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 |