Llama 3 Lumimaid 8B (extended)

Description

The NeverSleep team is back, with a Llama 3 8B finetune trained on their curated roleplay data. Striking a balance between eRP and RP, Lumimaid was designed to be serious, yet uncensored when necessary.

To enhance it's overall intelligence and chat capability, roughly 40% of the training data was not roleplay. This provides a breadth of knowledge to access, while still keeping roleplay as the primary strength.

Usage of this model is subject to Meta's Acceptable Use Policy.

Note: this is an extended-context version of Llama 3 Lumimaid 8B. It may have higher prices and 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?

You would first need to install the marshmallow and marshmallow_dataclass libraries. Here's a sample code snippet:

import json
from marshmallow import Schema, fields, validates
from marshmallow_dataclass import dataclass, class_schema
from datetime import datetime

from marshmallow import ValidationError

@dataclass
class Order:
    created: datetime
    amount: float

order_schema = class_schema(Order)

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

person_schema = class_schema(Person)

# Load the data from json
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.loads(json_data)

person = Person.schema.load(json_data)

This will load the 'person' dataclass with the provided data from the json data. In your actual application, you would not create Person instance from a json string, but you would get the json data then serialize it to Person.

In order to serialize the data to the json string, use:

```Python
import json
from marshmallow import validate

@dataclass
class Order:
created: datetime
amount: float

order_schema = class_schema(Order)

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

person_schema = class_schema(Person)

person = Person(first_name="Jane", last_name="Smith", date_of_birth=datetime.strptime("03/16/1977", "%m/%d/%Y"), orders=[Order(datetime.strptime("2024-01-25T15:00:04", "%Y-%m-%dT%H:%M:%S"), 100.00)]

result = person_schema().dump(person)
print(json.dumps(result, default=str))
```<|eot_id|>

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 given marking scheme:

  1. The solution defines two dataclasses, Order and Person. (2 marks)

  2. The solution defines two schema classes using class_schema for both Order and Person. (2 marks)

  3. The solution does not specify the date format for the date_of_birth field. It should have used format="%m/%d/%Y". (0 marks)

  4. The decimal field (amount) is not defined with as_string=True. (0 marks)

  5. The schema and dataclasses are not linked with a @post_load method on the schema classes. Instead, it uses marshmallow_dataclass which automatically generates schemas from dataclasses. While this is a valid approach, it doesn't meet the specific requirement. (0 marks)

  6. Additional points:

  7. The solution imports necessary libraries.
  8. It demonstrates how to load data from JSON and how to serialize data to JSON.
  9. It uses marshmallow_dataclass, which simplifies the process but doesn't meet all the specific requirements.
    (1 mark)

Here's the breakdown of the marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 2,
  "Date format specified": 0,
  "Decimal field as_string": 0,
  "post_load method": 0,
  "Additional points": 1
}

Final score:

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

The solution demonstrates a good understanding of using dataclasses with marshmallow, but it misses some specific requirements from the marking scheme. It uses marshmallow_dataclass, which is a convenient approach but doesn't allow for the detailed control specified in the requirements. The solution could be improved by manually defining the schemas with the required field options and adding @post_load methods to link the schemas with the dataclasses.

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

This proposed solution attempts to address the problem of defining data classes and schemas for serialization and deserialization of a JSON structure using Python with the marshmallow library. Let's evaluate the solution based on the given marking scheme:

  1. Defining Data Classes for Person and Order:
  2. Two dataclasses (Person and Order) are defined correctly.

  3. Data types specified in the data classes are partially incorrect (the amount should be Decimal instead of float). Marks: 1.5/2

  4. Defining Schema Classes for Person and Order:

  5. Schema classes are automatically generated using class_schema from marshmallow_dataclass, which correctly links the schema to the data class.

  6. Correct usage of marshmallow for schema generation using class_schema. Marks: 2/2

  7. Specification of Date Format for date_of_birth Field:

  8. The solution does not customize the date_of_birth field to enforce the MM/DD/YYYY format within the schema. The custom format is used in code that assigns data to an object not for schema validation or transformation.

  9. No usage of fields.Date() with format specification. Marks: 0/1

  10. Decimal Field Specification:

  11. The amount field is incorrectly declared as float in the data classes and does not use Decimal or specify as_string=True in the schema.

  12. This part is completely missing in the solution. Marks: 0/1

  13. Linking Schema and Data Classes via a @post_load Method:

  14. There is no use of the @post_load method that would allow for custom processing after loading data into the schema. This technique is essential for creating nested data class objects from loaded data.

  15. No implementation or mention of @post_load. Marks: 0/2

  16. Additional Considerations:

  17. Error in the JSON parsing and serialization code (json_data is not correctly loaded into person).
  18. Missing closing single quote mark after the JSON string causing a syntax error.
  19. Correct handling of OrderedDict and customized JSON date formats where applied directly.
  20. Adequate demonstration of loading data into the schema and serialization, but with syntax and logical errors. Marks: 0.5/2

Total Score: 4/10

Comment:
The solution shows a basic understanding of using marshmallow and dataclasses for data serialization and deserialization tasks, but it includes several critical errors and misimplementations. Especially, the handling of specific data types like Date and Decimal is not implemented as requested, and crucial functionalities like @post_load are missing entirely. Additionally, the provided sample contains syntax errors that would prevent it from running successfully. Importantly, ensuring the code is error-free and meets the specific requirements of the task is necessary for a complete and functional solution.

{
  "score": 4
}

I can help you with that. First, you need to install the required libraries if you haven't already: marshmallow, marshmallow-field, and python-dateutil. This can be done using pip:

pip install marshmallow marshmallow-field python-dateutil

Here's how you can create the dataclasses using marshmallow:

import datetime as dt
from marshmallow import Schema, fields
from marshmallow import DateTime, Date, Decimal
from marshmallow.fields import List

class OrderSchema(Schema):
    created = fields.DateTime(deserialize=lambda x: dt.datetime.fromisoformat(x + '+00:00'), format='%Y-%m-%dT%H:%M:%S')
    amount = fields.Decimal(as_string=True)

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.Nested(OrderSchema, default=[], allow_null=True)

person_data = {
    "first_name": "Jane",
    "last_name": "Smith",
    "date_of_birth": "1977-03-16",  # This should match the expected format for date
    "orders": [
        {
          "created": "2024-01-25T15:00:04", 
          "amount": "100.00"
        }
    ]
}

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

In this script, we create two schema objects OrderSchema and PersonSchema. OrderSchema is for the order object and PersonSchema is for the person main object.

In OrderSchema, we use DateTime for the created field and Decimal for the amount. The deserialize function in DateTime is to convert the provided time into a datetime object. The format argument is for outputting time in the desired format. For the date_of_birth field, we use DateField.

In PersonSchema, we use Nested to create nested fields for the other schema objects and their fields. The person_data is loaded into the schema and the output is printed. The first_name, last_name, date_of_birth and orders objects are given their expected field names and fields from the provided json. Experience is gained through Python programming.<|eot_id|>

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) (0.96¢)

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

  1. The solution doesn't define dataclasses. It only defines schema classes. (0/2 marks)

  2. The solution correctly defines two schema classes: OrderSchema and PersonSchema. (2/2 marks)

  3. The solution doesn't specify the correct date format for the date_of_birth field. It should be format="%m/%d/%Y", but it's not specified at all. (0/1 mark)

  4. The decimal field correctly uses as_string=True. (1/1 mark)

  5. The solution doesn't implement @post_load methods to link schemas with dataclasses. (0/2 marks)

  6. Additional points:

  7. The solution correctly uses nested fields for the orders.
  8. It provides a sample of how to use the schema to load data.
  9. It includes instructions for installing necessary libraries.
    (2/2 marks)

Comments:
The solution provides a good start with marshmallow schemas, but it misses some key requirements. It doesn't create dataclasses, which was a main part of the task. The date format for date_of_birth is not correctly specified. The solution also lacks the @post_load decorators to create dataclass instances from the loaded data. However, it does show a good understanding of how to set up and use marshmallow schemas.

Here's the breakdown of marks:

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

Final score:

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

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

The solution uses Python and the Marshmallow library to parse and validate JSON data against defined schemas and rules. Here's the evaluation based on the marking scheme:

  1. Dataclasses Definition:

    • There are no actual Python dataclasses defined in the given solution; instead, it uses marshmallow.Schema for structure validation. Therefore, this part of the solution does not meet the criteria.

  2. Schema Classes Definition:

    • Two schema classes, OrderSchema and PersonSchema, are correctly defined in the provided solution. These handle serialization and deserialization processes.

  3. Date Format Specification for date_of_birth:

    • The date_of_birth field in the PersonSchema is defined as Date. However, the solution fails to specify the format ("%m/%d/%Y") for parsing date_of_birth which is crucial as per the problem statement.

  4. Decimal Field Specification:

    • The amount field in the OrderSchema is correctly set up with a Decimal field, and the as_string=True is set properly, which is required.

  5. Linking Schema with Dataclasses via @post_load:

    • The solution does not include a @post_jobs method or any similar approach to map the loaded data into instances of actual dataclasses (since no dataclasses were defined).

  6. Extra Aspects:

    • The provided solution demonstrates the usage of nested schemas (OrderSchema in PersonSchema) which is correctly done. However, handling of formats and types could be improved. The schema misrepresents the date of birth (slightly wrong format used in person_data). Also, no error or exception handling logic is provided or mentioned.

Overall, the key objectives and several small but critical details, like proper formatting of dates and the full utilization of dataclasses for structured data representation as per problem specifications, had not been achieved.

Score:

{
  "score": 4
}

You can use the marshmallow library in python to define your dataclasses from a JSON object. Here is how you can do it:

First, install the marshmallow library using pip:

pip install marshmallow

Then, you can use marshmallow to create a schema for your data. In this case, you want to transform the json to dataclass. A simple way to do this is by creating two classes: one for the individual data and one for the list of orders. Here's how you can do it:

from datetime import datetime
from marshmallow import Schema, fields, post_load
from marshmallow_jsonapi import Relationship

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

    @post_load
    def deserialize_amount(self, data):
        return Order(**data)


class Order:
    def __init__(self, created, amount):
        self.created = datetime.strptime(created, '%Y-%m-%dT%H:%M:%S')
        self.amount = float(amount)


class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = Relationship(Many=OrderSchema, Wage="orders")

class Person:
    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 = orders


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

schema = PersonSchema()
data = schema.load(json.loads(person))

print(data.orders)

In this code:

  • The OrderSchema defines how to transform the JSON into the Order class.
  • The Order class is a simple dataclass to hold the created date and the amount as a decimal. Its constructor is used by marshmallow to create instances of the class from the JSON data.
  • The PersonSchema defines how to transform the JSON into the Person class.
  • The Person class is a simple dataclass to hold the name, date of birth and the list of orders. Its constructor is used by marshmallow to create instances of the class from the JSON data.
  • Then the schema is used to load the JSON string into a Person object and print the orders.

Note that this requires Python 3.6 (because it uses the dataclasses module) or Python 3.5 (by using thirdparty library dataclasses). The @dataclass decorator should be used after Python 3.7.<|eot_id|>

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¢)

Thank you for providing the solution. I'll evaluate it based on the marking scheme you've provided.

  1. The solution defines two classes, Order and Person, which serve as dataclasses. However, they are not explicitly decorated with @dataclass. (1/2 marks)

  2. The solution defines two schema classes, OrderSchema and PersonSchema. (2/2 marks)

  3. The solution does not specify the date format for the date_of_birth field in the schema. It only handles the format in the Person class constructor. (0/1 mark)

  4. The decimal field (amount) is not specified with as_string=True. (0/1 mark)

  5. The solution includes a @post_load decorator for the OrderSchema, linking it to the Order class. However, it's missing for the PersonSchema. (1/2 marks)

  6. Additional observations (2/2 marks):

  7. The solution imports marshmallow_jsonapi, which wasn't mentioned in the problem statement.
  8. The Relationship field is used incorrectly (should be many=True instead of Many=OrderSchema).
  9. The solution attempts to handle date parsing in the class constructors, which is not the ideal approach when using marshmallow.

Here's the breakdown of marks:

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

Final score:

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

The solution demonstrates a good understanding of the basic structure needed but misses some key requirements and contains a few errors. It would benefit from closer attention to the specific requirements and correct usage of marshmallow features.

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

The solution attempts to make use of the marshmallow library for handling JSON data through serialization and deserialization. Here’s a detailed review based on the criteria provided:

  1. Dataclass Definition: Although the classes for Person and Order were attempted, the actual use of the dataclasses module is missing as no @dataclass decorator or related functionality from the module is applied. Also, the Order class should have used float or Decimal but has done incorrect type processing in implementation.
    Score: 0/2

  2. Schema Class Definition: There are schema definitions for both Order and Person as OrderSchema and PersonSchema. However, the correct format for a relationship in marshmallow was not correctly used (Relationship does not exist in marshmallow, and instead fields.Nested() should be utilized).
    Score: 1/2

  3. Date Format Specification: The date format for date_of_birth has been omitted entirely in the PersonSchema. There's no specification that matches the required format ("%m/%d/%Y").
    Score: 0/1

  4. Decimal Field as String: Although the code mentions amount as a decimal, there is no usage of as_string=True in the fields.Decimal for ensuring string representation of decimals.
    Score: 0/1

  5. Linking Schema and Dataclasses with @post_load: There are @post_load methods but they incorrectly handle the deserialization, especially not converting data types correctly (e.g., manually parsing datetime and misinterpreting the decimal type as float). This does not fully exploit marshmallow’s capabilities and leads to the erroneous definition of data types.
    Score: 0.5/2

  6. Additional Elements: The sample code is partially correct in showing how marshmallow can load and parse JSON data. However, it has major issues in usage of correct library methods and handling of datatypes. There’s also an error in importing marshmallow_jsonapi, which is not a default part of marshmallow and requires a separate installation (if correct, but improperly utilized here).
    Score: 0.5/2

Given these points, the code does not accurately solve the task described.

{
  "score": 2/10
}
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