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---
license: mit
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: supercot-lora
results: []
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# mistral-v0.1-supercot-lora
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the [supercot](https://huggingface.co/datasets/kaiokendev/SuperCOT-dataset) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9790
## Model description
SuperCOT is a LoRA trained with the aim of making Mistral follow prompts for Langchain better, by infusing chain-of-thought datasets, code explanations and instructions, snippets, logical deductions and Alpaca GPT-4 prompts. It uses a mixture of the following datasets:
https://huggingface.co/datasets/QingyiSi/Alpaca-CoT
- Chain of thought QED
- Chain of thought Aqua
- CodeAlpaca
https://huggingface.co/datasets/neulab/conala
- Code snippets
https://huggingface.co/datasets/yahma/alpaca-cleaned
- Alpaca GPT4
## Intended uses & limitations
The model will show biases similar to those exhibited by the base model. It is not intended for supplying factual information or advice in any form.
## Training and evaluation data
[kaiokendev/SuperCOT-dataset](https://huggingface.co/datasets/kaiokendev/SuperCOT-dataset)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7661 | 0.06 | 20 | 1.5173 |
| 0.7681 | 0.12 | 40 | 1.2323 |
| 0.6647 | 0.18 | 60 | 1.1306 |
| 0.6742 | 0.24 | 80 | 1.0847 |
| 0.6995 | 0.3 | 100 | 1.0573 |
| 0.6883 | 0.36 | 120 | 1.0412 |
| 0.6437 | 0.42 | 140 | 1.0375 |
| 0.6331 | 0.48 | 160 | 1.0186 |
| 0.6686 | 0.54 | 180 | 1.0153 |
| 0.6767 | 0.6 | 200 | 1.0042 |
| 0.7037 | 0.66 | 220 | 1.0023 |
| 0.6994 | 0.72 | 240 | 1.0014 |
| 0.7012 | 0.78 | 260 | 0.9996 |
| 0.6599 | 0.84 | 280 | 0.9926 |
| 0.6401 | 0.9 | 300 | 0.9913 |
| 0.6665 | 0.96 | 320 | 0.9910 |
| 0.5771 | 1.02 | 340 | 0.9907 |
| 0.6286 | 1.08 | 360 | 0.9830 |
| 0.6064 | 1.14 | 380 | 0.9865 |
| 0.5976 | 1.19 | 400 | 0.9802 |
| 0.5512 | 1.25 | 420 | 0.9817 |
| 0.6333 | 1.31 | 440 | 0.9810 |
| 0.5883 | 1.37 | 460 | 0.9817 |
| 0.5822 | 1.43 | 480 | 0.9783 |
| 0.5878 | 1.49 | 500 | 0.9757 |
| 0.5951 | 1.55 | 520 | 0.9753 |
| 0.6466 | 1.61 | 540 | 0.9719 |
| 0.6246 | 1.67 | 560 | 0.9681 |
| 0.627 | 1.73 | 580 | 0.9705 |
| 0.6214 | 1.79 | 600 | 0.9691 |
| 0.6558 | 1.85 | 620 | 0.9709 |
| 0.5736 | 1.91 | 640 | 0.9674 |
| 0.6188 | 1.97 | 660 | 0.9674 |
| 0.5293 | 2.03 | 680 | 0.9742 |
| 0.5463 | 2.09 | 700 | 0.9766 |
| 0.5184 | 2.15 | 720 | 0.9776 |
| 0.5349 | 2.21 | 740 | 0.9783 |
| 0.5536 | 2.27 | 760 | 0.9794 |
| 0.5016 | 2.33 | 780 | 0.9822 |
| 0.5075 | 2.39 | 800 | 0.9795 |
| 0.5529 | 2.45 | 820 | 0.9808 |
| 0.5168 | 2.51 | 840 | 0.9784 |
| 0.5416 | 2.57 | 860 | 0.9793 |
| 0.4845 | 2.63 | 880 | 0.9804 |
| 0.5487 | 2.69 | 900 | 0.9801 |
| 0.5313 | 2.75 | 920 | 0.9797 |
| 0.5449 | 2.81 | 940 | 0.9790 |
| 0.5303 | 2.87 | 960 | 0.9795 |
| 0.5599 | 2.93 | 980 | 0.9795 |
| 0.544 | 2.99 | 1000 | 0.9790 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
### 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}
}
``` |