---
title: Use Mistral-7B from Hugging Face
description: How to deploy an LLM application using Mistral-7B from Hugging Face'
---

This tutorial guides you through deploying am LLM application in agenta using Mistral-7B from Hugging Face.



1. **Set up a Hugging Face Account**  
   Sign up for an account at [Hugging Face](https://huggingface.co/).

2. **Access Mistral-7B Model**  
   Go to the [Mistral-7B-v0.1 model page](https://huggingface.co/mistralai/Mistral-7B-v0.1?text=My+name+is+Thomas+and+my+main).

3. **Deploy the Model**  
   Choose the 'Deploy' option and select 'Interface API'.

4. **Generate an Access Token**  
   If you haven't already, create an Access Token on Hugging Face.

5. **Initial Python Script**  
   Start with a basic script to interact with Mistral-7B:

   ```python
   import requests

   API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-v0.1"
   headers = {"Authorization": f"Bearer [Your_Token]"}

   def query(payload):
       response = requests.post(API_URL, headers=headers, json=payload)
       return response.json()

   output = query({"inputs": "Your query here"})
   ```

6. **Integrate Agenta SDK**  
   Modify the script to include the Agenta SDK:

   ```python
   import agenta as ag
   import requests

   API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-v0.1"
   headers = {"Authorization": f"Bearer [Your_Token]"}

   ag.init()
   ag.config.register_default(
       prompt_template=ag.TextParam("Summarize the following text: {text}")
   )

   @ag.entrypoint
   def generate(text: str) -> str:
       prompt = ag.config.prompt_template.format(text=text)
       payload = {"inputs": prompt}
       response = requests.post(API_URL, headers=headers, json=payload)
       return response.json()[0]["generated_text"]
   ```

7. **Deploy with Agenta**  
   Execute these commands to deploy your application:
   ```
   agenta init
   agenta variant serve app.py
   ```

8. **Interact with Your App**  
   Access a playground for you app in agenta in https://cloud.agenta.ai/apps (or locally if you have self-hosted agenta).

   ![Image of Mistral-7B Interface](/images/tutorial-deploy-mistral-model/playground_mistral.png)

9. **Evaluate Model Performance**  
   Use Agenta's tools to create a test set and evaluate your LLM application.

   ![Image of Evaluation Interface](/images/tutorial-deploy-mistral-model/evaluation_mistral.png)

10. **Publish Your Variant**  
    To deploy, go to the playground, click 'Publish', and choose the deployment environment.

    ![Image of Deployment Options](/images/tutorial-deploy-mistral-model/deploy_dev_mistral.png)

11. **Access the Endpoints**  
    In the 'Endpoints' section, select your environment and use the provided endpoint to interact with your LLM app.

    ![Image of Endpoint Access](/images/tutorial-deploy-mistral-model/endpoint_mistral.png)

Note that you can compare this model to other LLMs by serving other variants of the app. Just create a new `app_cohere.py` for instance using cohere model and serve it in the same app using `agenta variant serve app_cohere.py`.
