Create README.md

README.md

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+---
+tags:
+- sql
+- spider
+- text-to-sql
+- sql finetune
+- 8bit
+datasets:
+- spider
+- richardr1126/spider-skeleton-context-instruct
+library_name: transformers
+license: bigcode-openrail-m
+---
+### Spider Skeleton Wizard Coder 8bit Summary
+
+- This model was created by finetuning [WizardLM/WizardCoder-15B-V1.0](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0) on an enhanced Spider context training dataset: [richardr1126/spider-skeleton-context-instruct](https://huggingface.co/datasets/richardr1126/spider-skeleton-context-instruct).
+- Finetuning was performed using QLoRa on 3x RTX6000 48GB.
+- This is the bitsandbytes 8-bit version of the model. It needs to be loaded onto a GPU for it to work.
+- If you want just the QLoRa/LoRA adapter: [richardr1126/spider-skeleton-wizard-coder-qlora](https://huggingface.co/richardr1126/spider-skeleton-wizard-coder-qlora)
+
+### Running the GGML model
+- The best way to run this model is to use the [4-bit GGML version](https://huggingface.co/richardr1126/spider-skeleton-wizard-coder-ggml) on [koboldcpp](https://github.com/LostRuins/koboldcpp), with CuBlas support.
+
+### Spider Dataset
+
+[Spider](https://arxiv.org/abs/1809.08887) is a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 Yale students
+The goal of the Spider challenge is to develop natural language interfaces to cross-domain databases.
+
+This dataset was used to finetune this model.
+
+## Citations
+
+```
+@misc{luo2023wizardcoder,
+      title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct}, 
+      author={Ziyang Luo and Can Xu and Pu Zhao and Qingfeng Sun and Xiubo Geng and Wenxiang Hu and Chongyang Tao and Jing Ma and Qingwei Lin and Daxin Jiang},
+      year={2023},
+}
+```
+```
+@article{yu2018spider,
+  title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task},
+  author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and others},
+  journal={arXiv preprint arXiv:1809.08887},
+  year={2018}
+}
+```
+```
+@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}
+}
+```
+
+## Disclaimer
+
+The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced by any version of WizardCoder is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.