Toppy M 7B (nitro)

Description

A wild 7B parameter model that merges several models using the new task_arithmetic merge method from mergekit.
List of merged models:
- NousResearch/Nous-Capybara-7B-V1.9
- HuggingFaceH4/zephyr-7b-beta
- lemonilia/AshhLimaRP-Mistral-7B
- Vulkane/120-Days-of-Sodom-LoRA-Mistral-7b
- Undi95/Mistral-pippa-sharegpt-7b-qlora

#merge #uncensored

Note: this is a higher-throughput version of Toppy M 7B. 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 (3.0)

To generate the dataclasses using the marshmallow library in Python, you can follow these steps:

First, you need to install the marshmallow library using pip:

pip install marshmallow

Create a new Python file and import the necessary modules:

from marshmallow import Schema, fields, DateTime, Date, Decimal
from typing import List

Define the schema for the JSON data:

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = Date()

class OrderSchema(Schema):
    created = DateTime()
    amount = Decimal()

class JSONDataSchemaWithDataclasses(Schema):
    person = fields.Nested(PersonSchema)
    orders = fields.Nested(OrderSchema, many=True)

Use the JSON class method to load the JSON data and generate the dataclasses:

json_data = '''
{
  'first_name': 'Jane',
  'last_name': 'Smith',
  'date_of_birth': '03/16/1977',
  'orders': [

    {
      'created': '2024-01-25T15:00:04',
      'amount': '100.00'
    }
  ]
}
'''

data = JSON.load(json_data)

Now, the data variable contains the JSON data with the corresponding dataclasses generated by the marshmallow library. You can access the fields using Python's dataclass syntax, like this:

print(data.person.first_name)  # Output: Jane
print(data.orders[0].created)  # Output: datetime.datetime(2024, 1, 25, 15, 0, 4)
print(data.orders[0].amount)   # Output: Decimal('100.00')

This approach allows you to easily manipulate and validate the data using Python's dataclasses and the marshmallow library's validation features.

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) (1.03¢)

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

Defining two dataclasses (2 marks):
The solution doesn't explicitly define dataclasses. It only defines schemas.
Marks: 0/2
Defining two schema classes (2 marks):
The solution defines PersonSchema and OrderSchema correctly.
Marks: 2/2
Specifying date format for date_of_birth (1 mark):
The solution doesn't specify the date format for date_of_birth.
Marks: 0/1
Decimal field with as_string=True (1 mark):
The solution doesn't use as_string=True for the Decimal field.
Marks: 0/1
Linking schema and dataclasses with @post_load (2 marks):
The solution doesn't use @post_load to link schemas and dataclasses.
Marks: 0/2
Anything else (2 marks):
The solution imports necessary modules.
It provides a structure for handling nested data with JSONDataSchemaWithDataclasses.
It shows how to load JSON data, although it uses an undefined JSON class.
It gives an example of how to access the data, assuming dataclasses were created.
Marks: 1/2

Comments:
The solution provides a basic structure for using marshmallow to handle JSON data, but it falls short in several key areas. It doesn't actually create dataclasses, doesn't use the required date format or decimal settings, and doesn't link schemas to dataclasses. The example for loading and accessing data is conceptually correct but relies on undefined elements.

{
  "marks 1": 0,
  "marks 2": 2,
  "marks 3": 0,
  "marks 4": 0,
  "marks 5": 0,
  "marks 6": 1
}

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

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

The provided solution has various issues and mistakes when compared to the problem statement and the requirements laid out in the original task. Here are the details:

Dataclasses Definition: The solution did not define any dataclasses for Person and Order. It purely relies on Marshmallow schemas without utilizing Python's dataclasses module. This would be incorrect since the problem explicitly asked for dataclasses. (0/2)

2, Schema Classes: There are schema definitions through Marshmallow (PersonSchema, OrderSchema, and JSONDataSchemaWithDataclasses). However, the nested schema pattern used here might not be perfectly aligned with the specified JSON structure. There's no clear definition or distinction in how Person and Orders tightly integrate beyond what appears to be attempt at a wrap-around schema (JSONDataDataclasses). (1/2)

Date Format Specification: Although the solution includes a Date() field for date_of_birth, it doesn't specify the format (%m/%d/%Y) as required by the problem. (0/1)
Decimal Field Representation: The amount field in Order is defined using Decimal(), but it doesn't use as_string=True parameter. This is crucial to meet the problem's requirement that the amount should remain as a string type decimal. (0/1)
Linking Schema with Dataclasses via @post_load: There are no @post_load methods or any actual linkage of data loaded through schemas to dataclasses, which is a primary requirement of using Marshmallow to create and populate dataclass instances. (0/2)
Additional Criteria:
Improper library usage: the use of JSON.load(json_data) is not correct in Python; it seems to be a mix-up with standard JSON library usage.
Misrepresentation in following Marshmallow's loading pattern.
Good comments in the solution script that explains some steps. However, significant errors in execution, especially misunderstanding marshmallow JSON loading and the difference between raw Python json handling.
No error handling, type enforcement, or advanced validation features of Marshmallow utilized well. (0/2)

Overall, the provided solution does not fulfill the requirements properly from both the technical and functional perspectives, missing core elements of the assignment guidelines.

{
  "score": 1
}

Response 2 (4.0)

To achieve this using the Marshmallow library in Python, you first need to install it using pip:

pip install marshmallow

Then, you can write a Schema class to define your dataclasses. Here's how:

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

class OrderSchema(Schema):
    created = fields.DateTime(format="iso")
    amount = fields.Decimal()

class PersonSchema(Schema):
    first_name = fields.String(required=True)
    last_name = fields.String(required=True)
    date_of_birth = fields.Date()
    orders = fields.Nested(OrderSchema, many=True)

Now, you can create a Person object from your JSON data using these schema classes:

import json
from marshmallow import Marshmallow

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

person_schema = PersonSchema()
person_data = person_schema.load(json.loads(json_data))

print(person_data.orders) # prints a list of Order instances

This will parse the JSON data into a Person instance and the Order instances within the orders list. You can now easily access the attributes of each dataclass and perform any required operations on them.

Note: When Marshmallow encounters dates in ISO format, it converts them to Python's built-in datetime objects. Similarly, Decimal types are used for the amount attribute instead of float or string, making it easier to perform accurate mathematical calculations with them.

I hope this helps you get started with using Marshmallow in your Python project! Let me know if you have any further questions.

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) (1.08¢)

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

The solution doesn't define dataclasses. It only defines Schema classes. (0/2 marks)
The solution correctly defines two schema classes: OrderSchema and PersonSchema. (2/2 marks)
The solution doesn't specify the date format for the date_of_birth field. It should have used fields.Date(format="%m/%d/%Y"). (0/1 mark)
The decimal field doesn't include as_string=True. It should have been fields.Decimal(as_string=True). (0/1 mark)
The solution doesn't include @post_load methods to link the schema and dataclasses. (0/2 marks)
Additional points:
The solution correctly imports necessary modules.
It provides a sample of how to use the schema to load JSON data.
It includes helpful comments explaining the behavior of Marshmallow.
(2/2 marks)

Breakdown of marks:

{
  "Dataclass definition": 0,
  "Schema definition": 2,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema and dataclass linking": 0,
  "Additional points": 2
}

Score:

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

Comments:
The solution provides a good start with the Marshmallow library, correctly defining schema classes and demonstrating how to use them. However, it misses several key requirements:

It doesn't define dataclasses, which was a core requirement.
It doesn't specify the correct date format for the date_of_birth field.
The decimal field isn't configured with as_string=True.
There's no linking between schema and dataclasses using @post_load decorators.

These omissions significantly impact the solution's ability to meet the specified requirements. The solution does provide good additional context and usage examples, which is valuable. To improve, the solution should incorporate dataclasses, correct field specifications, and use @post_load decorators to create the required objects.

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

The provided solution attempts to use the marshmallow library to deserialize JSON data into Python objects using schemas. Here’s the assessment based on the given marking scheme:

Dataclasses Definition: The solution does not define any Python dataclasses for Person or Order. It directly uses marshmallow schemas but does not include any use of the dataclasses module.
Marks: 0/2
Schema Classes Definition: The solution does define marshmallow schemas for Person and Order.
Marks: 2/2
Date Format Specification: The date_of_birth field in the PersonSchema uses the default date parsing and does not specify the required format "%m/%d/%Y".
Marks: 0/1
Decimal Field Configuration: The amount field in OrderSchema is specified as a Decimal type but lacks the as_string=True attribute to ensure it is treated as a string during serialization and deserialization.
Marks: 0/1
Post Load Method: The solution does not use or mention a @post_load method to link schema loading to instantiation of dataclasses. As noted, dataclasses themselves are not implemented.
Marks: 0/2
Additional Comments:
The example provided uses incorrect JSON format in json_data (it should use double quotes for JSON keys and values instead of single quotes).
Also, the PersonSchema and OrderSchema fields and methods for data handling are oversimplified and lack the correct handling mechanisms for peculiar data types such as dates and decimals with specified formats.
There is an import statement from marshmallow import Marshmallow, which is incorrect as Marshmallow is not a valid import from marshmallow. This could confuse users or lead to runtime errors.
The absence of the correct handling for date formats and decimal values as well as custom load behavior via @post_load means the schema does not fully perform the transformations as intended in the task.
Marks: 0/2

Overall, while the solution provides a basic structure using marshmallow schemas, it fails to meet several specific requirements of the task, especially those regarding proper data types and formats handling, as well as linking deserialization with dataclasses. The solution also contains a significant technical inaccuracy with imports.

Final score:

{
  "score": 2
}

Response 3 (5.0)

To generate dataclasses using the marshmallow library in Python, you can follow these steps:

First, install the marshmallow library if you haven't already:

pip install marshmallow

Create a new Python file and import the necessary modules:

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

Define your dataclasses using the marshmallow.Schema class:

class OrderSchema(Schema):
    created = fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = fields.Decimal(precision=2)

class CustomerSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.Nested('OrderSchema', many=True)

Use the post_load decorator to convert the date_of_birth field from a string to a datetime.date object:

@post_load
def make_customer(data, **kwargs):
    data['date_of_birth'] = datetime.strptime(data['date_of_birth'], '%m/%d/%Y').date()
    return CustomerSchema(**data)

Now you can load your JSON data into the CustomerSchema:

json_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '201/25/2024 15:00:04',
            'amount': '100.00'
        }
    ]
}

customer = CustomerSchema().load(json_data)

Now customer is a Customer object with the data from your JSON, and the date_of_birth and created fields are of the correct types. You can access the fields using dot notation, like customer.first_name or customer.orders[0].amount.

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) (1.00¢)

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

Defining two dataclasses:
The solution doesn't explicitly define dataclasses. It uses Schemas instead.
Score: 0/2
Defining two schema classes:
The solution correctly defines OrderSchema and CustomerSchema.
Score: 2/2
Specifying date format for date_of_birth:
The solution correctly specifies the format as "%m/%d/%Y".
Score: 1/1
Decimal field with as_string=True:
The solution doesn't include as_string=True for the amount field.
Score: 0/1
Linking schema and dataclasses with @post_load:
The solution includes a @post_load method, but it doesn't create a dataclass instance.
Score: 1/2
Anything else:
The solution correctly handles the DateTime format for the 'created' field.
It provides a basic example of how to use the schema to load JSON data.
Score: 1/2

Comments:
The solution provides a good start with marshmallow schemas, but it misses some key requirements. It doesn't use dataclasses, which was a primary requirement. The @post_load method is present but doesn't create a dataclass instance. The decimal field is missing the as_string=True parameter. However, it does handle the date and datetime formats correctly and provides a basic usage example.

Breakdown of marks:

{
  "Defining dataclasses": 0,
  "Defining schema classes": 2,
  "Date format for date_of_birth": 1,
  "Decimal field with as_string": 0,
  "Linking schema and dataclasses": 1,
  "Additional considerations": 1
}

Final score:

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

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

The provided solution has a few considerations and errors:

Dataclass definition: The solution incorrectly implies that data classes can be defined using marshmallow.Schema. It fails to define the Python dataclasses for both the person (Customer) and the order. This is incorrect as Schema in Marshmallow is for serialization and deserialization, not equivalent to or a substitute for a dataclass. Marks are lost here.
Schema classes definition: The schema classes (OrderSchema and CustomerSchema) are well defined for marshmallow. However, the assumption that these are dataclasses is incorrect.
Date and DateTime Formats: The schemas correctly specify the format for the date_of_birth using fields.Date(format='%m/%d/%Y') and for the created field within OrderSchema using a precise datetime format which is correctly recognized.
Decimal field specification: The solution fails to specify amount as a string output via the as_string=True parameter in the definition of the Decimal field. This is essential to handle the decimal amount correctly as specified in the problem requirement.
Post-load method usage: The post_load decorator is used, but it is misapplied. Instead of transforming and returning a real person or order dataclass instance from the data, it seems to just transform a string date into a datetime date, through the wrong schema (return CustomerSchema(**,data)) mentions using schema as a dataclass which is incorrect. Furthermore, the provided post_load method has a logical mistake and does not convert the data to a dataclass instance.
Additional Requirements:
Loading and using the JSON correctly is suggested, but the method and explanation are mistaken.
No real dataclasses for Customer and Order are implemented or linked.

Given these points, the solution, though attempting to use Marshmallow correctly in aspects of schema creation and data conversion, severely misinterprets the use of dataclasses with schemas and mismanages the implementation. Therefore, assigning marks:

Dataclasses definition: 0/2
Schema classes definition: 2/2
Specifying date format: 1/1
Decimal field with as_string=True: 0/1
Use of @post_load for linking schemas and dataclasses: 0/2
Additional correct practices and problem understanding: {given the extensive confusion about dataclasses and schemas, the incorrect usage of JSON data, incorrect post_load implementation, big mistakes in data handling}: 0/2

{
  "score": 3
}

1.00	0.89	0.90
0.89	1.00	0.88
0.90	0.88	1.00

Toppy M 7B (nitro)

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