Instructions to use DS-Archive/mistral-v0.1-supercot-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DS-Archive/mistral-v0.1-supercot-lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DS-Archive/mistral-v0.1-supercot-lora")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DS-Archive/mistral-v0.1-supercot-lora")
model = AutoModelForCausalLM.from_pretrained("DS-Archive/mistral-v0.1-supercot-lora")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DS-Archive/mistral-v0.1-supercot-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DS-Archive/mistral-v0.1-supercot-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DS-Archive/mistral-v0.1-supercot-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DS-Archive/mistral-v0.1-supercot-lora

SGLang

How to use DS-Archive/mistral-v0.1-supercot-lora with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "DS-Archive/mistral-v0.1-supercot-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DS-Archive/mistral-v0.1-supercot-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "DS-Archive/mistral-v0.1-supercot-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DS-Archive/mistral-v0.1-supercot-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DS-Archive/mistral-v0.1-supercot-lora with Docker Model Runner:
```
docker model run hf.co/DS-Archive/mistral-v0.1-supercot-lora
```

mistral-v0.1-supercot-lora

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the supercot 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

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}
}

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