MythoMax 13B (nitro)

Description

One of the highest performing and most popular fine-tunes of Llama 2 13B, with rich descriptions and roleplay. #merge

Note: this is a higher-throughput version of MythoMax 13B. 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)

Using the marshmallow library in Python, you can define your dataclasses as follows:

from marshmallow import Schema, fields, validates_datetime

class CustomerInformation(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(format="%m/%d/%Y", validate=validates_datetime)
    orders = fields.List(fields.Nested(OrderInfo))

class OrderInfo(Schema):
    created = fields.DateTime(format="%Y-%m-%dT%H:%M:%S")
    amount = fields.Float(only_numbers=True)

You can then load the JSON data into a CustomerInformation object using marshmallow's load_instance method:

data = {
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  orders: [
    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
  ]
}
customer_info = CustomerInformation().load_instance(data)

This will create a CustomerInformation object with the loaded values.

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¢)

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

The solution does not define dataclasses. It only defines Schema classes. (0/2 marks)
The solution correctly defines two schema classes: CustomerInformation and OrderInfo. (2/2 marks)
The solution correctly specifies the date format for the date_of_birth field as "%m/%d/%Y". (1/1 mark)
The solution does not use as_string=True for the decimal field. Instead, it uses only_numbers=True which is not the correct approach. (0/1 mark)
The solution does not include @post_load methods to link schemas and dataclasses. (0/2 marks)
Additional observations:
The solution uses validates_datetime for date_of_birth, which is good for validation.
The solution uses load_instance() method, which is not a standard marshmallow method. It should be load() instead.
(1/2 marks)

Breakdown of marks:

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

Score:

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

Comments:
The solution partially addresses the problem but misses several key requirements. It correctly defines schema classes and specifies the date format, but it doesn't create dataclasses, doesn't properly handle the decimal field, and doesn't use @post_load methods to create dataclass instances. The use of load_instance() is incorrect, and the solution doesn't fully meet the requirements of using marshmallow to generate dataclasses from the JSON input. Significant improvements are needed to fully solve the problem as specified.

openai/gpt-4-0125-preview says: (2.0) ()

The provided solution includes the use of the Marshmallow library to handle JSON data deserialization into Python objects. Here is a detailed evaluation based on the marking scheme:

Definition of two dataclasses, one for the person and one for the order:
The provided example uses CustomerInformation and OrderInfo, but these are incorrectly defined as Schema instead of dataclasses. So, marks cannot be given here.
Score: 0/2
Definition of two schema classes, one for the person and one for the order:
The solution does define two schema classes (CustomerInformation and OrderInfo). However, since the problem involves also transforming data types (i.e., date, datetime, decimal), the usage of correct field types is critical but mishandled here.
Score: 1/2 (Partial credit because the entities are defined, albeit not in the best way to handle conversions correctly.)
Specification of the date format for the date_of_birth field:
The date format is specified correctly as %m/%d/%Y in the date_of_birth field.
Score: 1/1
Using the as_string=True for the decimal field:
The solution uses Float for the 'amount' field with parameter only_numbers=True, which is inappropriate. The correct usage should have been Decimal(as_string=True).
Score: 0/1
Linking schema and dataclasses using a @post_load method:
There is no use of a @post_load method in the provided solution to transform the loaded data into instance objects of the defined data or schema classes.
Score: 0/2
Any other aspects:
Incorrect usage of the marshmallow fields and methods, especially in data type conversions and the absence of proper dataclass handling.
Score: 0/2

Total Score: 2/10

{
  "score": 2
}

Response 2 (3.0)

To generate dataclasses from the given JSON data, we can use the marshmallow library in Python. Here's an example:

from marshmallow import fields, Schema, validate

class NameSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)

class OrderSchema(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Float(required=True)

class PersonSchema(Schema):
    name = NameSchema()
    orders = fields.List(fields.Nested(OrderSchema()))

validator = validate.SchemaValidator(PersonSchema())
data = {
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  orders: [
    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
  ]
}

person_data = person_schema.load(data)
print(person_data)

This code defines three dataclasses: NameSchema, OrderSchema, and PersonSchema. The PersonSchema class includes both the name and orders fields, which are populated with the values from the JSON data.

The validate function is used to validate the JSON data against the PersonSchema. The data variable contains the JSON data as a dictionary. The load method of the PersonSchema is used to load the JSON data into the PersonSchema object.

Finally, the person_data variable is printed to demonstrate that the dataclasses have been successfully generated from the JSON data.

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: (3.0) (0.88¢)

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

The solution defines three schema classes (NameSchema, OrderSchema, PersonSchema) but doesn't define any dataclasses.
The solution defines three schema classes correctly.
The solution doesn't specify the date format for the date_of_birth field.
The solution uses fields.Float for the amount field instead of fields.Decimal(as_string=True).
The solution doesn't include any @post_load methods to link schemas and dataclasses.
Additional observations:
The solution includes a validator using validate.SchemaValidator, which wasn't required.
The date_of_birth field is missing from all schemas.
The JSON data structure in the example code doesn't match the given JSON (nested 'name' object vs. separate first_name and last_name).

Here's the breakdown of marks:

{
  "Define two dataclasses": 0,
  "Define two schema classes": 2,
  "Specify date format for date_of_birth": 0,
  "Use Decimal field with as_string=True": 0,
  "Link schemas and dataclasses with @post_load": 0,
  "Additional observations": 1
}

Final score:

{
  "scores": [0, 2, 0, 0, 0, 1],
  "total_score": 3
}

The solution demonstrates some understanding of using marshmallow for schema definition but misses several key requirements from the problem statement and marking scheme. It doesn't create dataclasses, misses important field specifications, and doesn't link schemas to dataclasses. The total score is 3 out of 10.

openai/gpt-4-0125-preview says: (1.0) ()

The provided solution aims to solve the problem by defining schema classes using the marshmallow library to parse and validate JSON data in Python. However, it does not adhere to all the guidelines and requirements specified in the problem.

Defining two dataclasses: The solution does not define any Python dataclasses (dataclass from the dataclasses module). It only defines schema classes with marshmallow. (0/2)
Defining two schema classes: It defines three schema classes, NameSchema, OrderSchema, and PersonSchema. While it meets the requirement to some extent, the separation into separate schemas including a NameSchema is unnecessary and not directly requested. (1/2)
Specifying the date format for the date_of_birth field: This solution does not handle the date_of_birth field at all. It fails to include it in any schema, and consequently does not specify a date format. (0/1)
Decimal field needs as_string=True: The solution uses fields.Float for the amount field, which is incorrect both in type (should be Decimal) and does not use as_string=True to ensure it remains as a Decimal string format. (0/1)
@post_load method to link schema and dataclasses: There is no usage of the @post_load decorator to create instances of dataclasses, mainly because no dataclasses were defined in the first place. (0/2)
Anything else: They do attempt to implement the validation using validate.SchemaValidator, which isn't a standard class or method provided by marshmallow. This points towards either incorrect usage of library methods or a potential typo. The use of nested schemas for name incorrectly treats this data example, as the JSON does not nest first and last names under a separate key. (0/2)

Overall, the solution has significant misconceptions and does not correctly utilize the marshmallow library per the requirements. The schema implementation errors and misinterpretations lead to a solution that deviates markedly from the task objectives, including a lack of correct types handling and the use of non-existent functionality in marshmallow.

{
  "score": 1
}

Response 3 (6.0)

Here's an example of how you can define your dataclasses using Marshmallow:

from marshmallow import (
    fields,
    Schema,
    dataclass,
    post_dump,
    post_load
)

class PersonSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(required=True)
    orders = fields.List(fields.Nested(OrderSchema()))

class OrderSchema(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(required=True, allow_nan=True, attr_type=float)

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

    def to_dict(self):
        return {k: getattr(self, k) for k in PersonSchema().json_fields()}

    @classmethod
    def from_dict(cls, d):
        pers = cls(**d)
        return pers

This code defines two dataclasses: Person and Order. The Person class has a list of Order objects. The Order class contains a created datetime and an amount decimal. The PersonSchema is a Marshmallow schema that defines the JSON structure for a single person, including their name, date of birth, and a list of orders.

You can use this code like so:

person = Person(
    first_name='Jane',
    last_name='Smith',
    date_of_birth='1977-03-16',
    orders=[
        Order(
            created='2024-01-25T15:00:04',
            amount=100.00
        )
    ]
)

json_person = person.to_dict()

person2 = Person.from_dict(json_person)

This creates a Person object with the given data, then serializes it to JSON and deserializes it back again. The resulting Person object will have the same attributes as the original.

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) (1.06¢)

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

The solution defines two dataclasses, Person and Order. (2 marks)
The solution defines two schema classes, PersonSchema and OrderSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. It should have used format="%m/%d/%Y". (0 marks)
The decimal field does not have as_string=True. (0 marks)
The schema and dataclasses are not linked with a @post_load method on the schema classes. Instead, it uses custom to_dict and from_dict methods. (0 marks)
Additional points:
The solution includes import statements for necessary Marshmallow components. (1 mark)
It uses the @dataclass decorator for the Person class. (1 mark)

Here's the breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 2,
  "Date format specified": 0,
  "Decimal field configuration": 0,
  "Schema and dataclass linking": 0,
  "Additional points": 2
}

Final score:

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

Comments:
The solution provides a good starting point with the definition of dataclasses and schema classes. However, it misses some key requirements:

The date format for date_of_birth is not specified.
The decimal field is not configured with as_string=True.
The schema and dataclasses are not linked using @post_load decorators.

Instead of using Marshmallow's built-in deserialization methods, the solution implements custom to_dict and from_dict methods. While these can work, they don't take full advantage of Marshmallow's features and may not handle all edge cases correctly.

The solution could be improved by addressing these issues and making better use of Marshmallow's capabilities for serialization and deserialization.

openai/gpt-4-0125-preview says: (3.0) ()

The solution provided attempts to utilize Python's dataclass feature and the marshmallow library to define and serialize a complex data structure. Here's an evaluation based on the criteria given:

Dataclasses Definition:
The solution provides a dataclass for Person which is correct. However, it is incomplete as the Order dataclass is missing, although it is referenced in the Person dataclass. This is a significant omission.
Score: 1/2
Schema Classes Definition:
The solution correctly defines a PersonSchema, but partially for OrderSchema. The OrderSchema is used within PersonSchema, suggesting it exists, but it's not defined explicitly in the snippet provided which could lead to confusion or runtime errors.
Score: 1/2
Date and DateTime Format Specification:
The date_of_birth field in PersonSchema does not specify the format as "%m/%d/%Y". This is a requirement that was not met.
The created field in OrderSchema correctly uses fields.DateTime but does not explicitly show the datetime format used, which might be acceptable depending on implementation preference.
Score: 0/1
Decimal Field Handling:
The amount field in the OrderSchema uses fields.Decimal, which is correct, but it does not use as_string=True as specified in the requirements.
Score: 0/1
Post-Load Linking:
The code shows @post_dump and introduces some serialization methods but does not correctly implement or show usage of the @post_load method, which is crucial for proper linking and instantiation of dataclasses from schemas.
Score: 0/2
Additional Considerations:
The attempt to create custom methods to_dict and from_dict under Person dataclass shows effort towards serialization/deserialization, but this isn't utilizing marshmallow's capabilities directly (like load or dump methods in marshmallow).
The solution shows an attempt to integrate dataclasses with marshmallow, but miscommunications between the actual usage of schema, serialization, and deserialization logic indicate a misunderstanding or incomplete implementation.
Score: 1/2

Final Score:

{
  "score": 3
}

The solution partially meets some criteria but lacks full implementation and correct handling of specific requirements like decimal formatting and proper usage of marshmallow's serialization/deserialization capabilities including the @post_load decorator.

1.00	0.73	0.75
0.73	1.00	0.81
0.75	0.81	1.00

MythoMax 13B (nitro)

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