---
title: Explore Code Generation Using DeepSeek Coder Models
description: Explore how you can use AI models to generate code and work more efficiently.
image: https://developers.cloudflare.com/dev-products-preview.png
---

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### Tags

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# Explore Code Generation Using DeepSeek Coder Models

**Last reviewed:**  over 2 years ago 

A handy way to explore all of the models available on [Workers AI](https://developers.cloudflare.com/workers-ai) is to use a [Jupyter Notebook ↗](https://jupyter.org/).

You can [download the DeepSeek Coder notebook](https://developers.cloudflare.com/workers-ai/static/documentation/notebooks/deepseek-coder-exploration.ipynb) or view the embedded notebook below.

---

## Exploring Code Generation Using DeepSeek Coder

AI Models being able to generate code unlocks all sorts of use cases. The [DeepSeek Coder ↗](https://github.com/deepseek-ai/DeepSeek-Coder) models `@hf/thebloke/deepseek-coder-6.7b-base-awq` and `@hf/thebloke/deepseek-coder-6.7b-instruct-awq` are now available on [Workers AI](https://developers.cloudflare.com/workers-ai).

Let's explore them using the API!

Python

```

import sys

!{sys.executable} -m pip install requests python-dotenv


```

```

Requirement already satisfied: requests in ./venv/lib/python3.12/site-packages (2.31.0)

Requirement already satisfied: python-dotenv in ./venv/lib/python3.12/site-packages (1.0.1)

Requirement already satisfied: charset-normalizer<4,>=2 in ./venv/lib/python3.12/site-packages (from requests) (3.3.2)

Requirement already satisfied: idna<4,>=2.5 in ./venv/lib/python3.12/site-packages (from requests) (3.6)

Requirement already satisfied: urllib3<3,>=1.21.1 in ./venv/lib/python3.12/site-packages (from requests) (2.1.0)

Requirement already satisfied: certifi>=2017.4.17 in ./venv/lib/python3.12/site-packages (from requests) (2023.11.17)


```

Python

```

import os

from getpass import getpass


from IPython.display import display, Image, Markdown, Audio


import requests


```

Python

```

%load_ext dotenv

%dotenv


```

### Configuring your environment

To use the API you'll need your [Cloudflare Account ID ↗](https://dash.cloudflare.com) (head to Workers & Pages > Overview > Account details > Account ID) and a [Workers AI enabled API Token ↗](https://dash.cloudflare.com/profile/api-tokens).

If you want to add these files to your environment, you can create a new file named `.env`

Terminal window

```

CLOUDFLARE_API_TOKEN="YOUR-TOKEN"

CLOUDFLARE_ACCOUNT_ID="YOUR-ACCOUNT-ID"


```

Python

```

if "CLOUDFLARE_API_TOKEN" in os.environ:

    api_token = os.environ["CLOUDFLARE_API_TOKEN"]

else:

    api_token = getpass("Enter you Cloudflare API Token")


```

Python

```

if "CLOUDFLARE_ACCOUNT_ID" in os.environ:

    account_id = os.environ["CLOUDFLARE_ACCOUNT_ID"]

else:

    account_id = getpass("Enter your account id")


```

### Generate code from a comment

A common use case is to complete the code for the user after they provide a descriptive comment.

Python

```

model = "@hf/thebloke/deepseek-coder-6.7b-base-awq"


prompt = "# A function that checks if a given word is a palindrome"


response = requests.post(

    f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/{model}",

    headers={"Authorization": f"Bearer {api_token}"},

    json={"messages": [

        {"role": "user", "content": prompt}

    ]}

)

inference = response.json()

code = inference["result"]["response"]


display(Markdown(f"""

    ```python

    {prompt}

    {code.strip()}

    ```

"""))


```

Python

```

# A function that checks if a given word is a palindrome

def is_palindrome(word):

    # Convert the word to lowercase

    word = word.lower()


    # Reverse the word

    reversed_word = word[::-1]


    # Check if the reversed word is the same as the original word

    if word == reversed_word:

        return True

    else:

        return False


# Test the function

print(is_palindrome("racecar"))  # Output: True

print(is_palindrome("hello"))    # Output: False


```

### Assist in debugging

We've all been there, bugs happen. Sometimes those stacktraces can be very intimidating, and a great use case of using Code Generation is to assist in explaining the problem.

Python

```

model = "@hf/thebloke/deepseek-coder-6.7b-instruct-awq"


system_message = "The user is going to give you code that isn't working. Explain to the user what might be wrong"


code = """# Welcomes our user

def hello_world(first_name="World"):

    print(f"Hello, {name}!")

"""


response = requests.post(

    f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/{model}",

    headers={"Authorization": f"Bearer {api_token}"},

    json={"messages": [

        {"role": "system", "content": system_message},

        {"role": "user", "content": code},

    ]}

)

inference = response.json()

response = inference["result"]["response"]

display(Markdown(response))


```

The error in your code is that you are trying to use a variable `name` which is not defined anywhere in your function. The correct variable to use is `first_name`. So, you should change `f"Hello, {name}!"` to `f"Hello, {first_name}!"`.

Here is the corrected code:

Python

```

# Welcomes our user

def hello_world(first_name="World"):

    print(f"Hello, {first_name}")


```

Now, when you call `hello_world()`, it will print "Hello, World" by default. If you call `hello_world("John")`, it will print "Hello, John".

### Write tests!

Writing unit tests is a common best practice. With the enough context, it's possible to write unit tests.

Python

```

model = "@hf/thebloke/deepseek-coder-6.7b-instruct-awq"


system_message = "The user is going to give you code and would like to have tests written in the Python unittest module."


code = """

class User:


    def __init__(self, first_name, last_name=None):

        self.first_name = first_name

        self.last_name = last_name

        if last_name is None:

            self.last_name = "Mc" + self.first_name


    def full_name(self):

        return self.first_name + " " + self.last_name

"""


response = requests.post(

    f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/{model}",

    headers={"Authorization": f"Bearer {api_token}"},

    json={"messages": [

        {"role": "system", "content": system_message},

        {"role": "user", "content": code},

    ]}

)

inference = response.json()

response = inference["result"]["response"]

display(Markdown(response))


```

Here is a simple unittest test case for the User class:

Python

```

import unittest


class TestUser(unittest.TestCase):


    def test_full_name(self):

        user = User("John", "Doe")

        self.assertEqual(user.full_name(), "John Doe")


    def test_default_last_name(self):

        user = User("Jane")

        self.assertEqual(user.full_name(), "Jane McJane")


if __name__ == '__main__':

    unittest.main()


```

In this test case, we have two tests:

* `test_full_name` tests the `full_name` method when the user has both a first name and a last name.
* `test_default_last_name` tests the `full_name` method when the user only has a first name and the last name is set to "Mc" + first name.

If all these tests pass, it means that the `full_name` method is working as expected. If any of these tests fail, it

### Fill-in-the-middle Code Completion

A common use case in Developer Tools is to autocomplete based on context. DeepSeek Coder provides the ability to submit existing code with a placeholder, so that the model can complete in context.

Warning: The tokens are prefixed with `<｜` and suffixed with `｜>` make sure to copy and paste them.

Python

```

model = "@hf/thebloke/deepseek-coder-6.7b-base-awq"


code = """

<｜fim▁begin｜>import re


from jklol import email_service


def send_email(email_address, body):

    <｜fim▁hole｜>

    if not is_valid_email:

        raise InvalidEmailAddress(email_address)

    return email_service.send(email_address, body)<｜fim▁end｜>

"""


response = requests.post(

    f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/{model}",

    headers={"Authorization": f"Bearer {api_token}"},

    json={"messages": [

        {"role": "user", "content": code}

    ]}

)

inference = response.json()

response = inference["result"]["response"]

display(Markdown(f"""

    ```python

    {response.strip()}

    ```

"""))


```

Python

```

is_valid_email = re.match(r"[^@]+@[^@]+\.[^@]+", email_address)


```

### Experimental: Extract data into JSON

No need to threaten the model or bring grandma into the prompt. Get back JSON in the format you want.

Python

```

model = "@hf/thebloke/deepseek-coder-6.7b-instruct-awq"


# Learn more at https://json-schema.org/

json_schema = """

{

  "title": "User",

  "description": "A user from our example app",

  "type": "object",

  "properties": {

    "firstName": {

      "description": "The user's first name",

      "type": "string"

    },

    "lastName": {

      "description": "The user's last name",

      "type": "string"

    },

    "numKids": {

      "description": "Amount of children the user has currently",

      "type": "integer"

    },

    "interests": {

      "description": "A list of what the user has shown interest in",

      "type": "array",

      "items": {

        "type": "string"

      }

    },

  },

  "required": [ "firstName" ]

}

"""


system_prompt = f"""

The user is going to discuss themselves and you should create a JSON object from their description to match the json schema below.


<BEGIN JSON SCHEMA>

{json_schema}

<END JSON SCHEMA>


Return JSON only. Do not explain or provide usage examples.

"""


prompt = """Hey there, I'm Craig Dennis and I'm a Developer Educator at Cloudflare. My email is craig@cloudflare.com.

            I am very interested in AI. I've got two kids. I love tacos, burritos, and all things Cloudflare"""


response = requests.post(

    f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/{model}",

    headers={"Authorization": f"Bearer {api_token}"},

    json={"messages": [

        {"role": "system", "content": system_prompt},

        {"role": "user", "content": prompt}

    ]}

)

inference = response.json()

response = inference["result"]["response"]

display(Markdown(f"""

    ```json

    {response.strip()}

    ```

"""))


```

```

{

  "firstName": "Craig",

  "lastName": "Dennis",

  "numKids": 2,

  "interests": ["AI", "Cloudflare", "Tacos", "Burritos"]

}


```

```json
{"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"item":{"@id":"/directory/","name":"Directory"}},{"@type":"ListItem","position":2,"item":{"@id":"/workers-ai/","name":"Workers AI"}},{"@type":"ListItem","position":3,"item":{"@id":"/workers-ai/guides/","name":"Guides"}},{"@type":"ListItem","position":4,"item":{"@id":"/workers-ai/guides/tutorials/","name":"Tutorials"}},{"@type":"ListItem","position":5,"item":{"@id":"/workers-ai/guides/tutorials/explore-code-generation-using-deepseek-coder-models/","name":"Explore Code Generation Using DeepSeek Coder Models"}}]}
```