Instructions to use frankjosh/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use frankjosh/model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoderbase-1b") model = PeftModel.from_pretrained(base_model, "frankjosh/model") - Notebooks
- Google Colab
- Kaggle
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Download README.md from frankjosh/model: direct link, hf CLI and curl.
- Browser
- Download file 2.4 kB
-
https://huggingface.co/frankjosh/model/resolve/main/README.md
- Command line
-
hf download hf://frankjosh/model/README.md
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curl -L -o README.md https://huggingface.co/frankjosh/model/resolve/main/README.md
2.4 kB
| library_name: peft | |
| license: bigcode-openrail-m | |
| base_model: bigcode/starcoderbase-1b | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: model | |
| 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. --> | |
| # model | |
| 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: 0.9188 | |
| ## 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: Use 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 | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.7875 | 0.05 | 100 | 0.9020 | | |
| | 0.8714 | 0.1 | 200 | 0.9049 | | |
| | 0.9661 | 0.15 | 300 | 0.9036 | | |
| | 0.9201 | 0.2 | 400 | 0.9043 | | |
| | 0.9876 | 0.25 | 500 | 0.9063 | | |
| | 0.7047 | 0.3 | 600 | 0.9101 | | |
| | 0.9354 | 0.35 | 700 | 0.9089 | | |
| | 0.9068 | 0.4 | 800 | 0.9108 | | |
| | 0.9546 | 0.45 | 900 | 0.9116 | | |
| | 0.7253 | 0.5 | 1000 | 0.9141 | | |
| | 0.9153 | 0.55 | 1100 | 0.9136 | | |
| | 0.9123 | 0.6 | 1200 | 0.9148 | | |
| | 0.8961 | 0.65 | 1300 | 0.9148 | | |
| | 0.8893 | 0.7 | 1400 | 0.9178 | | |
| | 0.8112 | 0.75 | 1500 | 0.9178 | | |
| | 0.8803 | 0.8 | 1600 | 0.9167 | | |
| | 0.8693 | 0.85 | 1700 | 0.9167 | | |
| | 0.8919 | 0.9 | 1800 | 0.9181 | | |
| | 0.791 | 0.95 | 1900 | 0.9190 | | |
| | 0.9191 | 1.0 | 2000 | 0.9188 | | |
| ### Framework versions | |
| - PEFT 0.14.0 | |
| - Transformers 4.47.1 | |
| - Pytorch 2.5.1+cu121 | |
| - Datasets 3.2.0 | |
| - Tokenizers 0.21.0 |