Llama 3 Lumimaid 8B

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.

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 (5.0)

To solve this problem, we'll need to first install the marshmallow library and its dependencies. Here are the steps:

Install the necessary Python packages:

pip install marshmallow pip install marshmallow ๆ

Now you're ready to create your dataclasses using marshmallow. Here's how you can do it:

First, you need to install the module:

import datetime

Because date_of_birth needs to be in date format and created needs to be in datetime format, we're going to use Python's inbuilt datetime module.

Now, you'll need to create your schema for your JSON:

```
from marshmallow import Schema, fields

class PersonSchema(Schema):
first_name = fields.Str()
last_name = fields.Str()
date_of_birth = fields.Date('%m/%d/%Y') # FORMAT {'09/30/2019'}
class Meta:
fields = ('first_name', 'last_name', 'date_of_birth')

   orders = fields.Nested('OrderSchema', many=True, default=[])

class OrderSchema(Schema):
created = fields.DateTime('%Y-%m-%dT%H:%M:%S') # FORMAT {'2021-12-15 15:45:15'}
amount = fields.Decimal(as_string=True)
```

Here, we're defining two schemas, PersonSchema and OrderSchema. The PersonSchema represents your JSON and has fields for first_name, last_name, and date_of_birth. The date_of_birth is defined with the format '%m/%d/%Y', which represents the 'month/day/year' format. Similarly,OrderSchemais defined for your array of orders.OrderSchemahas fields forcreatedin aYYYY-MM-DDTHH:MM:SSformat and the amount in decimal format. Fields are declared based on their types (e.g.,Str, Date, DateTime, Decimal, etc).

The Meta class is used to postprocess the schema, and you define a list of fields you need. In this case, it's first_name, last_name, and date_of_birth.

Now, once the schema is defined, we can use it to dump and load our data:

```
from marshmallow import Schema, fields

# DEFINE SCHEMAS AS ABOVE

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()
loaded_person = person_schema.load(data)
```

After creating the person object, you can dump it back to JSON:

print(person_schema.dumps(loaded_person.data))

Remember to use .dumps if you want to convert the data to a JSON string. In this case, loaded_person.data represents the loaded person as a dictionary. person_schema.dumps() returns a JSON string.<|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) (1.02¢)

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

The solution does not define two dataclasses for the person and order. (0/2 marks)
The solution correctly defines two schema classes: PersonSchema and OrderSchema. (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 correctly includes as_string=True for the decimal field. (1/1 mark)
The solution does not implement @post_load methods to link the schema and dataclasses. (0/2 marks)
Additional points:
The solution correctly imports necessary modules.
It provides an example of how to use the schemas to load and dump data.
However, it doesn't create actual dataclasses, which was a key requirement.
(1/2 marks)

Comments:
The solution provides a good implementation of the schema classes using marshmallow, but it misses a crucial part of the requirements - creating dataclasses. It also doesn't link the schemas to dataclasses using @post_load decorators. The solution demonstrates how to use the schemas for serialization and deserialization, which is helpful, but not explicitly required in the problem statement.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 1,
  "Decimal field specification": 1,
  "@post_load implementation": 0,
  "Additional points": 1
}

Final score:

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

openai/gpt-4-0125-preview says: (6.0) ()

The provided solution does address the initial problem statement of processing JSON data and converting specified fields into date, datetime, and decimal formats using the marshmallow library. Here's the evaluation based on the provided scoring system:

Defines two dataclasses (2 marks): The solution does not explicitly define any dataclasses. It defines schema classes but not Python dataclasses. Therefore, this requirement is not met.
Defines two schema classes (2 marks): Yes, PersonSchema and OrderSchema are defined and accurately represent the person and order objects respectively.
Specifies the date format for the date_of_right field (1 mark): The solution correctly specifies the date format for the date_of_birth field using fields.Date('%m/%d/%Y').
The decimal field needs as_string=True (1 mark): The solution correctly sets as_string=True for the amount field in OrderSchema, ensuring that the decimal value is serialized as a string.
Link schema and dataclasses with @post_load (2 marks): The solution does not include the use of the @post_load decorator or any method to convert the loaded data into instances of a dataclass or any other structured object. This point is missing.
Additional elements (2 marks): The solution includes the necessary imports and demonstrates how to load and dump data using instantiated schemas. However, the explanation contains formatting issues and misrepresentation (e.g., loaded_person.data, which does not exist without the use of @post_load logic to actually create and populate an object). Additionally, incorrect installation instructions, such as pip install marshmallow ๆ suggest a mistake that may confuse readers.

Overall, the provided solution addresses some critical aspects of the task but fails in crucial areas like the proper implementation of dataclasses and their integration using Marshmallow’s @post_load. Hence, it scores as follows:

{
  "score": 6
}

Response 2 (7.0)

To achieve this, you would first need to create a DataClass using the dataclass decorator in Python, then use marshmallow's Schema to serialize and deserialize your objects:

from dataclasses import dataclass
from marshmallow import Schema, fields
from marshmallow import Schema, fields
from marshmallow import Schema, validate
from datetime import datetime
from ldap3.core.exceptions import SchemaMissingAttribute

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

@dataclass
class Order:
    created: datetime
    amount: float

class OrderSchema(Schema):
    class Meta:
        fields = ["created", "amount"]

    created = fields.DateTime()
    amount = fields.Decimal(missing=datetime.utcnow)

class PersonSchema(Schema):
    class Meta:
        fields = ["first_name", "last_name", "date_of_birth", "orders"]

    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format="%m/%d/%Y")
    orders = fields.Nested(OrderSchema, default=[])

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

schema = PersonSchema()

person_obj = schema.load(person_data)

print(person_obj)

This will create a Person object from the provided JSON data and store it in the person_obj variable.

Remember to install both dataclasses and marshmallow python packages if you haven't done so yet:

pip install dataclasses marshmallow

To deserialize the output back to the original JSON format, use the dumps key in the schema.

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

schema = PersonSchema()

person_obj = Person(first_name='Jane', last_name='Smith', date_of_birth=datetime(1977, 3, 16), orders=[Order(created=datetime.strptime('2024-01-25T15:00:04', '%Y-%m-%dT%H:%M:%S'), amount=100.0)]

person_json = schema.dumps(person_obj).data

print(person_json)

Remember to use the docker run command with the flask environment thusly (flask run) to run your application and install both the python packages properly (pip install dataclasses marshmallow).<|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: (7.0) (1.05¢)

I'll evaluate the 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 specifies the date format for the date_of_birth field correctly. (1 mark)
The decimal field (amount) is not specified with as_string=True. (0 marks)
The schema and dataclasses are not linked with a @post_load method on the schema classes. (0 marks)
Additional points:
The solution imports necessary modules.
It provides examples of how to use the schemas for serialization and deserialization.
It includes instructions for installing required packages.
(2 marks)

Here's the breakdown of marks:

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

The total score:

{
  "scores": [2, 2, 1, 0, 0, 2],
  "total_score": 7
}

Comments:
The solution provides a good foundation for using marshmallow with dataclasses. However, it misses a few key points:

The amount field in OrderSchema should be defined with as_string=True to match the requirement.
There's no @post_load method to create dataclass instances from the deserialized data.
The Order dataclass is referenced before it's defined, which would cause an error.
The missing parameter on the amount field in OrderSchema seems misplaced and incorrect.

Despite these issues, the solution demonstrates a good understanding of using marshmallow with dataclasses and provides helpful additional information.

openai/gpt-4-0125-preview says: (5.5) ()

The provided solution partially meets the criteria of the marshmallow deserialization task but has a number of critical errors and omissions:

Dataclasses Defined: The solution correctly defines two dataclasses, Person and Order. Thus, it fulfills this criterion.
Score Contribution: 2/2
Schema Classes Defined: Schema classes for Person and Order are defined aptly, matching with the dataclasses.
Score Contribution: 2/2
Date Format Specification: The PersonSchema correctly identifies and formats the date_of_birth field using fields.Date(format="%m/%d/%Y").
Score Contribution: 1/1
Decimal Field as String: The solution does not use as_string=True for the amount field in OrderSchema. This should be corrected to ensure that amount is handled as a string when deserialized, as requested.
Score Contribution: 0/1
Link Schema and Dataclasses using @post_load: There is no @post_load method in the schema to create the Person and Order dataclasses upon loading, which is crucial for correct ORM-like functionality in real-world applications using marshmallow.
**Score : 0/2
Additional Considerations:
- There are syntax errors and unreferenced imports, which could create confusion.
- There is an incorrect type for the amount field in Order; it should be Decimal instead of float to match the problem statement.
- The example instantiation of Person class uses float for amount, contrary to the request for Decimal.
- There's undue repetition of schema instantiation and unnecessary imports.
- Some additional real-world robustness such as error handling or example validation missing are not present.
- Score Contribution: 0.5/2

Overall, the solution correctly sets the primary structure of dataclasses and corresponding marshmallow schemas but fails to include critical functionalities like decimal handling as a string, object creation via @post_laoad post-processing, and misuses field types slightly (float instead of Decimal).

Based on the discrepancy from the ideal solution that accurately mirrors both the datatype requisites and marshmallow's true potential, I’d adjust the score accordingly.

{
  "score": 5.5
}

Response 3 (8.0)

Firstly, you need to install the required libraries:

pip install json
pip install dataclasses
pip install marshmallow
pip install marshmallow-enum-support
pip install python-dateutil

Here's how you can create the required dataclasses using the Marshmallow library:

from dataclasses import dataclass
from datetime import datetime
from dateutil.parser import parse
from marshmallow import Schema, fields

@dataclass
class Order:
    created: datetime
    amount:float

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

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.List(fields.Nested(OrderSchema))

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

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

person_dump = PersonSchema().dump(person)
print(person_dump)

This code generates two dataclasses Person and Order. The OrderSchema is used to serialize Order instances and the PersonSchema is used to serialize Person instances. They both use the fields class from marshmallow to specify how to convert fields to a JSON representation. The fields class takes the type of the field and the format of the field. The fields.Dict class is used for fields that are lists of objects (nested).
Also note that you must import the parse function from the dateutil.parser module and the Decimal from decimal module, and install marshmallow-enum-support.

You could use person_dump to get the JSON representation of the person.Memcpying the schema to fields.String gives us human-readable data "date_of_birth": "%m %d %Y", You can use dateutil.parser to parse your date string, which you can do by using a format tuple (('%m %d %Y').

You can also deserialize them by using PersonSchema().load() and OrderSchema().load()

You can also override the serialize method of your fields if you want to customize them.<|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: (8.0) (1.00¢)

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 correctly specifies the date format for the date_of_birth field as format='%m/%d/%Y'. (1 mark)
The decimal field (amount) is correctly defined with as_string=True. (1 mark)
The solution does not include a @post_load method to link the schema and dataclasses. (0 marks)
Additional points:
The solution imports necessary libraries. (+0.5 marks)
It provides an example of how to use the schemas for serialization. (+0.5 marks)
The solution mentions deserialization using load() method. (+0.5 marks)
It suggests overriding the serialize method for customization. (+0.5 marks)

Comments:
The solution covers most of the required elements, but it misses the crucial @post_load decorator to link the schemas with the dataclasses. This is an important aspect of using Marshmallow with dataclasses. Additionally, the OrderSchema is used before it's defined, which would cause an error. The import of json is unnecessary for this specific implementation.

Here's the breakdown of marks:

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

Final score:

{
  "scores": [2, 2, 1, 1, 0, 2],
  "total_score": 8
}

openai/gpt-4-0125-preview says: (7.0) ()

The solution provided offers a good start for converting a JSON object into usable Python classes using dataclasses and the Marshmallow library. However, there are several issues and aspects which deviate from the requirements and best practices:

Definition of Two Dataclasses: The solution correctly defines two dataclasses, Person and Order. (2 marks)
Definition of Two Schema Classes: There are two corresponding schema classes, PersonSchema and OrderSchema, as requested. (2 marks)
Specifying Date Format for date_of_birth: The solution correctly provides a format for the date_of_birth using fields.Date(format='%m/%d/%Y'). (1 mark)
Decimal Field with as_string=True: The solution sets as_string=True for the amount field in the OrderSchema, which aligns with the requirement. (1 mark)
Usage of @post_load Method: The solution does not implement any @post_load decorator to link the serialized/deserialized data with dataclass instantiation. This would typically be crucial for converting loaded data directly to dataclass instances. (-2 marks)
Additional Considerations:
The solution imports unnecessary libraries (marshmallow-enum-support, json) which are not used.
The amount field in Order dataclass incorrectly uses float, despite the requirement stating it should be a decimal.Decimal.
Some text appears copied and slightly modified from a Marshmallow tutorial, which introduces some inaccuracies and unclear suggestions (like mentioning a non-existent fields.Dict and confusing explanations about date parsing).
The use of parse for date_of_birth and created in the Person instance creation could be avoided with proper schema utilization.
Unnecessary comments and incorrect suggestions like using fields.String for date_of_birth or that overriding serialize method was never detailed or necessary. (-2 marks)

Given the areas of improvement, and considering both the fulfillment and inadequacies:

{
  "score": 7
}

1.00	0.83	0.88
0.83	1.00	0.85
0.88	0.85	1.00

Llama 3 Lumimaid 8B

Description

Most Similar

Also by neversleep

Model Consistency

Model Evaluations

claude-3-haiku
claude-3.5-sonnet	5.0	7.0	8.0
gpt-3.5-turbo-0125
gpt-4-0125-preview	6.0	5.5	7.0
gpt-4-1106-preview