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---
title: Mistral-7B Playground
description: Launch a text-generation Streamlit app using Mistral-7B
version: EN
---
## Try out this model on [VESSL Hub](https://vessl.ai/hub).
This example runs an app for inference using Mistral-7B which is an open-source LLM developed by [Mistral AI](https://mistral.ai/). The model utilizes a grouped query attention (GQA) and a sliding window attention mechanism (SWA), which enable faster inference and handling longer sequences at smaller cost than other models. As a result, it achieves both efficiency and high performance. Mistral-7B outperforms Llama 2 13B on all benchmarks and Llama 1 34B in reasoning, mathematics, and code generation benchmarks.
<img
className="rounded-md"
src="/images/mistral-streamlit.png"
/>
## Running the model
You can run the model with our quick command.
```sh
vessl run create -f mistral_7b.yaml
```
Here's a rundown of the `mistral_7b.yaml` file.
```yaml
name: mistral-7b-streamlit
description: A template Run for inference of Mistral-7B with streamlit app
resources:
cluster: vessl-gcp-oregon
preset: v1.l4-1.mem-42
image: quay.io/vessl-ai/hub:torch2.1.0-cuda12.2-202312070053
import:
/model/: hf://huggingface.co/VESSL/Mistral-7B
/code/:
git:
url: https://github.com/vessl-ai/hub-model
ref: main
run:
- command: |-
pip install -r requirements_streamlit.txt
streamlit run streamlit_demo.py --server.port 80
workdir: /code/mistral-7B
interactive:
max_runtime: 24h
jupyter:
idle_timeout: 120m
ports:
- name: streamlit
type: http
port: 80
```
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