Mixtral 8x7B Instruct (nitro)

Description

A pretrained generative Sparse Mixture of Experts, by Mistral AI, for chat and instruction use. Incorporates 8 experts (feed-forward networks) for a total of 47 billion parameters.

Instruct model fine-tuned by Mistral. #moe

Note: this is a higher-throughput version of Mixtral 8x7B Instruct. It may have higher prices and slightly different outputs.

Prompt

I need to take this json:

{
'first_name': 'Jane',
'last_name': 'Smith',
'date_of_birth': '03/16/1977',
orders: [
{
'created': '2024-01-25T15:00:04',
'amount': '100.00'
}
]}

And generate some dataclasses.

date_of_birth needs to be a date.
created is a datetime.
amount is a decimal.

How would I do this using the marshmallow library in python?

Response 1 (4.0)

First, you need to install the marshmallow library if you haven't already. You can do this by running:

pip install marshmallow

Next, you can create the necessary DataClass schemas and field definitions using the marshmallow library. Here's how you can define the DataClass schemas for the given JSON.

import datetime
from decimal import Decimal
from marshmallow import Schema, fields, pprint

# Define the inner Order schema
class OrderSchema(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(required=True, places=2)

# Define the main User schema
class UserSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(required=True, format='%m/%d/%Y')
    orders = fields.Nested(OrderSchema, many=True)

# Example JSON input and user schema initialization
json_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}

user_schema = UserSchema()

# Load the JSON input into the User schema
user_dict = user_schema.load(json_data)

# Log the user dictionary for demonstration
pprint(user_dict)

With this code, you've defined DataClass schemas using the marshmallow library. The date_of_birth field is a date, and the created field is a datetime, and the amount is a decimal. Make sure the JSON input provided has the correct format.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (4.0) (0.94¢)

Let's evaluate this solution based on the provided marking scheme:

The solution does not define two dataclasses. It only defines schemas using marshmallow's Schema class. (0/2 marks)
The solution correctly defines two schema classes: OrderSchema and UserSchema. (2/2 marks)
The solution correctly specifies the date format for the date_of_birth field using format='%m/%d/%Y'. (1/1 mark)
The solution does not include as_string=True for the decimal field. (0/1 mark)
The solution does not include @post_load methods to link the schemas with dataclasses. (0/2 marks)
Additional points:
The solution correctly imports necessary modules.
It demonstrates how to use the schema to load JSON data.
It includes a pprint statement to display the result.
(1/2 marks)

Breakdown of marks:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 1,
  "Decimal field": 0,
  "Post load method": 0,
  "Additional points": 1
}

Score:

{
  "scores": [0, 2, 1, 0, 0, 1],
  "total_score": 4
}

Comments:
The solution demonstrates a good understanding of how to use marshmallow to define schemas for JSON data. However, it misses several key requirements from the marking scheme. It doesn't define actual dataclasses, which was a core requirement. The decimal field is missing the as_string=True parameter. There's also no implementation of @post_load methods to create dataclass instances from the loaded data. While the solution would work for parsing the JSON, it doesn't fully meet the specified requirements for generating dataclasses using marshmallow.

openai/gpt-4-0125-preview says: (4.0) ()

The provided solution has implemented the use of the marshmallow library to serialize and deserialize JSON data which matches certain elements of the requirements, but there are several critical omissions based on the marking scheme provided.

Dataclasses Definition: The solution does not define Python dataclasses for the person and orders. Instead, it directly creates Schema classes for serialization. This does not meet the requirement to define data classes for person and order (0/2 marks).
Schema Classes Definition: Two schema classes, UserSchema and OrderSchema, are correctly defined which do align with the requirement (2/2 marks).
Date Format Specification: The date_of_birth field in the UserSchema correctly specifies the date format (format='%m/%d/%Y'). This meets the criteria laid out in the marking scheme (1/1 mark).
Decimal Field Specification: The amount field in the OrderSchema specifies that it is a decimal with two places, but it does not use as_string=True as required by the prompt. Thus, it does fulfill the correct type but does not meet the entire requirement as specified (0/1 mark).
Post Load Method: The solution does not define or utilize any @post_load methods to create or connect dataclasses upon loading data. The deserialization process currently results in a dictionary rather than instances of data classes, which is a key omission as per the instructions (0/2 marks).
Additional Aspects: While the solution handles some potentially incorrect input in terms of deserialization and directly uses Marshmallow’s pprint to demonstrate the deserialization output, the basic example does not add functionality like robust error handling, updates, or more complex operations on the data beyond deserialization (1/2 marks).

Given these observations, here is the final score:

{
  "score": 4
}

The solution effectively defines the schema for serialization but misses the mark on defining and using data classes, and it omits the @post_load method crucial for creating data class instances from the JSON input.

Response 2 (5.0)

First, you need to install the marshmallow library. You can do this by running the following command in your terminal:

pip install marshmallow

Next, you can define your dataclasses and schema as follows:

from datetime import datetime
from marshmallow import Schema, fields, DateTime, Decimal

class OrderSchema(Schema):
    created = DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = Decimal(as_string=True)

class UserSchema(Schema):
    first_name = fields.String()
    last_name = fields.String()
    date_of_birth = fields.Date()
    orders = fields.List(fields.Nested(OrderSchema))

# Initialize a user schema
user_schema = UserSchema()

# Initialize the JSON data
json_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '1977-03-16',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}

# Dump the JSON data into the user schema
user_data = user_schema.load(json_data)

# Print the user data
print(user_data)

In the above code, the OrderSchema class defines a schema for the order object, with created as a DateTime field, and amount as a Decimal field. The UserSchema class defines a schema for the user object, with first_name, last_name, and date_of_birth as String, String, and Date fields, respectively, and orders as a List of Nested OrderSchema objects.

To initialize the schema, we can use the Schema class's constructor and pass in the fields as a dictionary. To convert the JSON data into the user schema, we can use the load method of the Schema class, and then print the user data.

Note that the date_of_birth field in the JSON data is a string in the format 'YYYY-MM-DD'. In the schema, we can use the Date field type to convert this string to a datetime object. Similarly, the created field in the JSON data is a string in the format 'YYYY-MM-DDTHH:MM:SS'. To convert this string to a datetime object, we can use the DateTime field type with the format argument set to '%Y-%m-%dT%H:%M:%S'. The Decimal field type is used to convert the amount field in the JSON data to a Decimal object.

Finally, to convert the user data back into JSON data, we can use the dump method of the Schema class:

# Convert the user data into JSON data
json_data = user_schema.dump(user_data)

# Print the JSON data
print(json_data)

This will output the JSON data with the created and date_of_birth fields as strings, and the amount field as a decimal number. To convert these fields back to their respective types, we can use the load method of the Schema class, as shown above.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (5.0) (1.03¢)

Let's evaluate this solution based on the provided marking scheme:

The solution does not define dataclasses for the person and the order. It only defines schemas. (0 marks)
The solution correctly defines two schema classes: OrderSchema and UserSchema. (2 marks)
The solution does not specify the correct date format for the date_of_birth field. It uses the default format instead of the required "%m/%d/%Y". (0 marks)
The decimal field (amount) correctly uses as_string=True. (1 mark)
The solution does not implement @post_load methods to link schemas and dataclasses. (0 marks)
Additional points:
The solution correctly uses nested schemas for the orders.
It provides a complete example of how to use the schemas to load and dump data.
It includes instructions for installing the marshmallow library.
(2 marks)

Breakdown of marks:

{
  "Dataclasses": 0,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal field": 1,
  "Post_load methods": 0,
  "Additional points": 2
}

Score:

{
  "scores": [0, 2, 0, 1, 0, 2],
  "total_score": 5
}

Comments:
The solution provides a good starting point for using marshmallow to handle JSON data, but it misses several key requirements. It doesn't use dataclasses, which was a core part of the task. The date format for date_of_birth is incorrect, and there's no implementation of @post_load methods to create dataclass instances. While the solution demonstrates good understanding of marshmallow schemas and field types, it doesn't fully meet the specific requirements of the problem.

openai/gpt-4-0125-preview says: (5.0) ()

The solution provided attempts to use the marshmallow library to serialize and deserialize JSON data using defined schemas and Python data types. However, some discrepancies and omissions negatively impact the effectiveness and accuracy of the solution, based on the specified evaluation criteria.

Defining dataclasses: The solution does not explicitly define any dataclasses using Python's dataclasses module. It only defines OrderSchema and UserSchema. This is a significant oversight as explicit dataclasses for Order and User were expected.
Defining schema classes: The OrderSchema and UserSchema are indeed defined, satisfying this criterion. These schema classes are linked correctly with the structure of the JSON data.
Date format for date_of_birth: The format specified in the problem ("%m/%d/%Y") has not been explicitly set in the UserSchema. The solution uses the default date parsing without specifying this format, which would result in a parsing error given the format in which date_of_birth is provided in the original JSON data ("03/16/1977").
Decimal field with as_string=True: The solution correctly adds as_string=True for the Decimal field which matches the requirement to treat amount in the JSON data as a decimal while handling it as a string within the schema.
Linkage of schema and dataclasses via @post_load: The solution lacks the @post_load methods that should be used to instantiate dataclasses after loading the data. This is a key component for integrating marshmallow schemas with explicit dataclasses.
Additional relevant and correct elements: The solution appropriately uses nested fields for orders and correctly configures the type and formatting of created and amount. Usage of fields.Date() and handling of nested structures are correctly implemented.

Given these observations, the score for this solution, based on the stated criteria, is summarized below:

{
  "score": 5
}

Explanation: The solution gains marks for defining schema classes, handling decimals, parsing nested data, and using some correct types (though with incorrect or omitted formats). It loses marks for not defining dataclasses, not specifying the correct date format, and omitting the implementation of @post_load methods necessary for creating dataclass instances from loaded data.

Response 3 (6.0)

To create the required dataclasses using the marshmallow library in Python, you would first need to import the necessary classes and define the schema. Here is an example of how the schema and the corresponding Python classes can be created for your JSON:

import json
from datetime import datetime
from decimal import Decimal
from marshmallow import Schema, fields, pprint

class Order:
    def __init__(self, created, amount):
        self.created = created
        self.amount = Decimal(amount)

class User:
    def __init__(self, first_name, last_name, date_of_birth, orders):
        self.first_name = first_name
        self.last_name = last_name
        self.date_of_birth = datetime.strptime(date_of_birth, '%m/%d/%Y')
        self.orders = [Order(order['created'], order['amount']) for order in orders]

class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.Nested(lambda: OrderSchema(), many=True)

class OrderSchema(Schema):
    created = fields.DateTime()
    amount = fields.Decimal(as_string=True)

if __name__ == '__main__':
    data = json.loads('''
    {
      "first_name": "Jane",
      "last_name": "Smith",
      "date_of_birth": "03/16/1977",
      "orders": [
        {
          "created": "2024-01-25T15:00:04",
          "amount": "100.00"
        }
      ]
    }
    ''')
    user_schema = UserSchema()
    user = User(**data)
    result = user_schema.dump(user)
    pprint(result)

This example demonstrates the creation of the User and Order classes, as well as the corresponding UserSchema and OrderSchema. The User class has an __init__ method that initializes the object with the required attributes. The UserSchema class has the fields keys that map to the attributes of the User class. The datetime.strptime function is used to convert the date_of_birth string to a datetime object. The Order class has an __init__ method that initializes the object with the required attributes. The OrderSchema class has the fields keys that map to the attributes of the Order class. The Order class is wrapped in the lambda function and passed to the Nested field in the OrderSchema class to make it a nested schema. The as_string=True argument is used for the Decimal field to convert it to a string.

In the main block, the json.loads function is used to convert the JSON string to a Python dictionary. The UserSchema is used to deserialize the data, and the User object is created. The User object is then serialized back to a JSON string using the UserSchema.dump method. The pprint function is used for pretty printing the serialized output.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (6.0) (0.93¢)

I'll evaluate the solution based on the provided marking scheme:

Two dataclasses defined (User and Order): 2 marks
Two schema classes defined (UserSchema and OrderSchema): 2 marks
Date format for date_of_birth: 0 marks (not specified in the schema)
Decimal field with as_string=True: 1 mark
Schema and dataclasses linked with @post_load: 0 marks (not implemented)

Additional observations:
- The solution uses regular classes instead of dataclasses.
- The date_of_birth is parsed in the User class constructor, but not in the schema.
- The solution includes a working example of how to use the classes and schemas.

Here's the breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal field": 1,
  "Schema-dataclass linking": 0,
  "Additional features": 1
}

Final score:

{
  "scores": [2, 2, 0, 1, 0, 1],
  "total_score": 6
}

The solution demonstrates a good understanding of using marshmallow for serialization and deserialization, but it misses some key requirements from the marking scheme. The use of regular classes instead of dataclasses and the absence of @post_load decorators are the main areas for improvement. The solution also doesn't specify the date format in the schema as required. However, it does provide a functional implementation that handles the core requirements of the task.

openai/gpt-4-0125-preview says: (4.0) ()

The solution provided presents a basic usage of Python dataclasses along with the Marshmallow schema for serialization and deserialization. The provided example does not meet all the criteria set by the marking scheme, but we can evaluate it based on the expected requirements:

Definition of two dataclasses (User and Order):
The solution uses standard Python classes with manual initialization methods instead of using the dataclasses library. While functional, they do not fulfill the requirement of using Python's @dataclass decorator for automatic initialization and representation.
Score: 0/2
Definition of two schema classes (UserSchema and OrderSchema):
Both schema classes are properly defined, matching the attributes of the User and Order classes respectively.
Score: 2/2
Specification of the date format for the date_of_birth field:
The schema incorrectly uses fields.Date() without specifying the required format ("%m/%d/%Y") directly in the schema. The format is handled during manual initialization, which is not utilizing Marshmallow's formatting capabilities.
Score: 0/1
Decimal field with as_string=True:
The amount field in the OrderSchema class correctly uses fields.Decimal(as_string=True).
Score: 1/1
Linking schema and dataclasses with a @post_load method:
The solution does not use the @post_load method to connect schema classes and dataclasses. Instead, it manually creates instances within the __init__ methods. This approach bypasses one of Marshmallow's key features of data loading and object creation.
Score: 0/2
Additional considerations:
The code does not utilize advanced features of Marshmallow effectively (like automatic object creation through @post_load).
The program handles JSON parsing and serialization, which is correctly implemented.
Usage of lambda: OrderSchema() in Nested is appropriate for handling nested schemas.
Score: 1/2

Overall, the solution has covered many basic aspects but lacks compliance with best practices in using Marshmallow's features to their full potential, particularly with respect to dataclasses and the @post_load method.

{
  "score": 4
}

1.00	0.90	0.89
0.90	1.00	0.89
0.89	0.89	1.00

Mixtral 8x7B Instruct (nitro)

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