Instructions to use Liu-Xiang/llama7bit-lora-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Liu-Xiang/llama7bit-lora-sql with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "Liu-Xiang/llama7bit-lora-sql") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Liu-Xiang/llama7bit-lora-sql: direct link, hf CLI and curl.
- Browser
- Download file 2.7 kB
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https://huggingface.co/Liu-Xiang/llama7bit-lora-sql/resolve/main/README.md
- Command line
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hf download hf://Liu-Xiang/llama7bit-lora-sql/README.md
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curl -L -o README.md https://huggingface.co/Liu-Xiang/llama7bit-lora-sql/resolve/main/README.md
2.7 kB
| base_model: meta-llama/Llama-2-7b-hf | |
| datasets: | |
| - generator | |
| library_name: peft | |
| license: llama2 | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| model-index: | |
| - name: llama7bit-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. --> | |
| # llama7bit-lora-sql | |
| This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the generator dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3666 | |
| ## 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 | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 1.2114 | 0.06 | 20 | 0.8331 | | |
| | 0.6838 | 0.12 | 40 | 0.5232 | | |
| | 0.5107 | 0.17 | 60 | 0.4600 | | |
| | 0.4618 | 0.23 | 80 | 0.4289 | | |
| | 0.4412 | 0.29 | 100 | 0.4153 | | |
| | 0.425 | 0.35 | 120 | 0.4067 | | |
| | 0.4182 | 0.41 | 140 | 0.3956 | | |
| | 0.4137 | 0.47 | 160 | 0.3912 | | |
| | 0.4047 | 0.52 | 180 | 0.3865 | | |
| | 0.4034 | 0.58 | 200 | 0.3834 | | |
| | 0.3968 | 0.64 | 220 | 0.3833 | | |
| | 0.3954 | 0.7 | 240 | 0.3782 | | |
| | 0.3921 | 0.76 | 260 | 0.3756 | | |
| | 0.3877 | 0.82 | 280 | 0.3730 | | |
| | 0.3849 | 0.87 | 300 | 0.3722 | | |
| | 0.3831 | 0.93 | 320 | 0.3714 | | |
| | 0.3787 | 0.99 | 340 | 0.3702 | | |
| | 0.3677 | 1.05 | 360 | 0.3692 | | |
| | 0.3632 | 1.11 | 380 | 0.3686 | | |
| | 0.3611 | 1.17 | 400 | 0.3677 | | |
| | 0.3588 | 1.22 | 420 | 0.3669 | | |
| | 0.3579 | 1.28 | 440 | 0.3666 | | |
| | 0.3551 | 1.34 | 460 | 0.3666 | | |
| | 0.3586 | 1.4 | 480 | 0.3665 | | |
| | 0.3581 | 1.46 | 500 | 0.3666 | | |
| ### Framework versions | |
| - PEFT 0.8.2 | |
| - Transformers 4.37.2 | |
| - Pytorch 2.2.2+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.2 |