Instructions to use SamChen888/Gemma-RDE-Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SamChen888/Gemma-RDE-Finetuned with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-9b-it-bnb-4bit") model = PeftModel.from_pretrained(base_model, "SamChen888/Gemma-RDE-Finetuned") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from SamChen888/Gemma-RDE-Finetuned: direct link, hf CLI and curl.
- Browser
- Download file 1.32 kB
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https://huggingface.co/SamChen888/Gemma-RDE-Finetuned/resolve/main/README.md
- Command line
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hf download hf://SamChen888/Gemma-RDE-Finetuned/README.md
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curl -L -o README.md https://huggingface.co/SamChen888/Gemma-RDE-Finetuned/resolve/main/README.md
1.32 kB
| library_name: peft | |
| license: other | |
| base_model: unsloth/gemma-2-9b-it-bnb-4bit | |
| tags: | |
| - llama-factory | |
| - lora | |
| - unsloth | |
| - generated_from_trainer | |
| model-index: | |
| - name: gemma2-9b-finetuned | |
| 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. --> | |
| # gemma2-9b-finetuned | |
| This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) on the treino_pt_rde dataset. | |
| ## 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: 3e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 32 | |
| - 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 | |
| - num_epochs: 3.0 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.12.0 | |
| - Transformers 4.46.1 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 3.1.0 | |
| - Tokenizers 0.20.3 |