Instructions to use migaraa/Gemma2B-LORAfied with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use migaraa/Gemma2B-LORAfied with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2b") model = PeftModel.from_pretrained(base_model, "migaraa/Gemma2B-LORAfied") - Notebooks
- Google Colab
- Kaggle
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Download README.md from migaraa/Gemma2B-LORAfied: direct link, hf CLI and curl.
- Browser
- Download file 2.2 kB
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https://huggingface.co/migaraa/Gemma2B-LORAfied/resolve/main/README.md
- Command line
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hf download hf://migaraa/Gemma2B-LORAfied/README.md
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curl -L -o README.md https://huggingface.co/migaraa/Gemma2B-LORAfied/resolve/main/README.md
2.2 kB
| license: gemma | |
| library_name: peft | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| - ipex | |
| - GPU Max 1100 | |
| datasets: | |
| - generator | |
| base_model: google/gemma-2b | |
| model-index: | |
| - name: Gemma2B-LORAfied | |
| 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. --> | |
| # Gemma2B-LORAfied | |
| This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.0206 | |
| ## Training Hardware | |
| This model was trained using: | |
| - GPU: Intel(R) Data Center GPU Max 1100 | |
| - CPU: Intel(R) Xeon(R) Platinum 8480+ | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 1e-05 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 8 | |
| - total_train_batch_size: 16 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_ratio: 0.05 | |
| - training_steps: 1480 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 2.927 | 1.64 | 100 | 2.5783 | | |
| | 2.4568 | 3.28 | 200 | 2.2983 | | |
| | 2.2609 | 4.92 | 300 | 2.1769 | | |
| | 2.1671 | 6.56 | 400 | 2.1051 | | |
| | 2.1065 | 8.2 | 500 | 2.0739 | | |
| | 2.0844 | 9.84 | 600 | 2.0567 | | |
| | 2.0643 | 11.48 | 700 | 2.0455 | | |
| | 2.0511 | 13.11 | 800 | 2.0374 | | |
| | 2.0435 | 14.75 | 900 | 2.0318 | | |
| | 2.0304 | 16.39 | 1000 | 2.0276 | | |
| | 2.0245 | 18.03 | 1100 | 2.0248 | | |
| | 2.0247 | 19.67 | 1200 | 2.0228 | | |
| | 2.0096 | 21.31 | 1300 | 2.0212 | | |
| | 2.0183 | 22.95 | 1400 | 2.0206 | | |
| ## Framework versions | |
| - PEFT 0.10.0 | |
| - Transformers 4.39.3 | |
| - Pytorch 2.0.1a0+cxx11.abi | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 |