Instructions to use Liu-Xiang/deci7bit-lora-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Liu-Xiang/deci7bit-lora-sql with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Deci/DeciLM-7B") model = PeftModel.from_pretrained(base_model, "Liu-Xiang/deci7bit-lora-sql") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Liu-Xiang/deci7bit-lora-sql: direct link, hf CLI and curl.
- Browser
- Download file 1.97 kB
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https://huggingface.co/Liu-Xiang/deci7bit-lora-sql/resolve/main/README.md
- Command line
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hf download hf://Liu-Xiang/deci7bit-lora-sql/README.md
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curl -L -o README.md https://huggingface.co/Liu-Xiang/deci7bit-lora-sql/resolve/main/README.md
1.97 kB
| base_model: Deci/DeciLM-7B | |
| datasets: | |
| - generator | |
| library_name: peft | |
| license: apache-2.0 | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| model-index: | |
| - name: deci7bit-lora-sql | |
| 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. --> | |
| # deci7bit-lora-sql | |
| This model is a fine-tuned version of [Deci/DeciLM-7B](https://huggingface.co/Deci/DeciLM-7B) on the generator dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3613 | |
| ## 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: 8 | |
| - eval_batch_size: 8 | |
| - seed: 1399 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 32 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 100 | |
| - training_steps: 500 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 0.7762 | 0.0565 | 20 | 0.5080 | | |
| | 0.4829 | 0.1129 | 40 | 0.4278 | | |
| | 0.4283 | 0.1694 | 60 | 0.4019 | | |
| | 0.4102 | 0.2258 | 80 | 0.3903 | | |
| | 0.4 | 0.2823 | 100 | 0.3856 | | |
| | 0.3919 | 0.3387 | 120 | 0.3800 | | |
| | 0.3876 | 0.3952 | 140 | 0.3773 | | |
| | 0.3845 | 0.4517 | 160 | 0.3741 | | |
| | 0.3801 | 0.5081 | 180 | 0.3665 | | |
| | 0.3733 | 0.5646 | 200 | 0.3613 | | |
| | 0.3697 | 0.6210 | 220 | 0.3613 | | |
| ### Framework versions | |
| - PEFT 0.12.0 | |
| - Transformers 4.44.0 | |
| - Pytorch 2.2.2+cu121 | |
| - Datasets 2.21.0 | |
| - Tokenizers 0.19.1 |