diff --git a/docs/developer_guides/tutorials/deploy-mistral-model.mdx b/docs/developer_guides/tutorials/deploy-mistral-model.mdx
index da7e6730fc..9212a7d49c 100644
--- a/docs/developer_guides/tutorials/deploy-mistral-model.mdx
+++ b/docs/developer_guides/tutorials/deploy-mistral-model.mdx
@@ -6,76 +6,6 @@ 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. Create a [Hugging Face Account](https://huggingface.co/)
-
-2. Navigate 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. Click on the 'Deploy' option, and then select 'Interface API'
-
-4. Create an Access Token (If you haven't already)
-
-5. Initially, you might start with a simple Python script to interact with the model:
-
-```
-import requests
-
-API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-v0.1"
-headers = {"Authorization": "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. Modify the script and add the Agenta SDK
-
-```
-import agenta as ag
-import requests
-
-API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-v0.1"
-headers = {"Authorization": "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 to Agenta
-
-```
-agenta init
-agenta variant serve app.py
-```
-
-8. Now, Interact with your app either locally http://localhost or in cloud https://cloud.agenta.ai/apps
-
-<img height="600" src="/images/tutorial-deploy-mistral-model/playground_mistral.png" />
-
-9. You can create a test set and evaluate the model's performance using Agenta's evaluation techniques
-
-<img height="600" src="/images/tutorial-deploy-mistral-model/evaluation_mistral.png" />
-
-10. If you want to deploy the variant, navigate to the playground, Click on 'Publish' and choose the envirenment environment to which you wish to deploy
-
-<img height="600" src="/images/tutorial-deploy-mistral-model/deploy_dev_mistral.png" />
-
-11. Go to 'Endpoints' section, select the environment then use the provided endpoint to send requests to the LLM app
-
-<img height="600" src="/images/tutorial-deploy-mistral-model/endpoint_mistral.png" />
-
-
-
 
 1. **Set up a Hugging Face Account**  
    Sign up for an account at [Hugging Face](https://huggingface.co/).