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README.md

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+
+### SuperCOT LoRA
+SuperCOT is a LoRA I trained with the aim of making LLaMa follow prompts for Langchain better, by infusing chain-of-thought datasets, code explanations and instructions, snippets, logical deductions and Alpaca GPT-4 prompts.
+Trained against LLaMa 30B 4-bit for 3 epochs with cutoff length 1024, using a mixture of the following datasets:
+
+[https://huggingface.co/datasets/QingyiSi/Alpaca-CoT](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
+
+Chain of thought QED
+
+Chain of thought Aqua
+
+CodeAlpaca
+
+[https://huggingface.co/datasets/neulab/conala](https://huggingface.co/datasets/neulab/conala)
+
+Code snippets
+
+[https://huggingface.co/datasets/yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned)
+
+Alpaca GPT4
+
+You should prompt the LoRA the same way you would prompt Alpaca or Alpacino:
+
+```
+Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+<instruction>
+
+### Input:
+<any additional context. Remove this if it's not neccesary>
+
+### Response:
+<make sure to leave a single new-line here for optimal results>
+```
+
+### Citations
+Alpaca COT datasets
+```
+@misc{alpaca-cot,
+  author = {Qingyi Si, Zheng Lin },
+  school = {Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China},
+  title = {Alpaca-CoT: An Instruction Fine-Tuning Platform with Instruction Data Collection and Unified Large Language Models Interface},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/PhoebusSi/alpaca-CoT}},
+}
+```
+Stanford Alpaca
+```
+@misc{alpaca,
+  author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
+  title = {Stanford Alpaca: An Instruction-following LLaMA model},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
+}
+```
+Google FLAN
+```
+@inproceedings{weifinetuned,
+  title={Finetuned Language Models are Zero-Shot Learners},
+  author={Wei, Jason and Bosma, Maarten and Zhao, Vincent and Guu, Kelvin and Yu, Adams Wei and Lester, Brian and Du, Nan and Dai, Andrew M and Le, Quoc V},
+  booktitle={International Conference on Learning Representations}
+}
+```