---
tags:
- LoRA
- QLoRa
- Merged LoRA Model
model-index:
- name: sql-guanaco-13b-merged
  results: []
datasets:
- richardr1126/sql-create-context_guanaco_style
spaces:
- richardr1126/sql-guanaco-13b-demo
---

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# sql-guanaco-13b-merged

- This is a merged LoRA model that can be used with AutoModelForCausalLM or LlamaModelForCausalLM.
- It is a combination of [richardr1126/guanaco-13b-merged](https://huggingface.co/richardr1126/guanaco-13b-merged) + [richardr1126/lora-sql-guanaco-13b-adapter](https://huggingface.co/richardr1126/lora-sql-guanaco-13b-adapter).
- This LoRA was fine-tuned using QLoRA techniques on the [richardr1126/sql-create-context_guanaco_style](https://huggingface.co/datasets/richardr1126/sql-create-context_guanaco_style) dataset.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 0.03
- training_steps: 1875
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3

## Citation

```bibtex
@article{dettmers2023qlora,
  title={QLoRA: Efficient Finetuning of Quantized LLMs},
  author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
  journal={arXiv preprint arXiv:2305.14314},
  year={2023}
}
```