Phi-3 Medium 128K Instruct

Description

Phi-3 128K Medium is a powerful 14-billion parameter model designed for advanced language understanding, reasoning, and instruction following. Optimized through supervised fine-tuning and preference adjustments, it excels in tasks involving common sense, mathematics, logical reasoning, and code processing.

At time of release, Phi-3 Medium demonstrated state-of-the-art performance among lightweight models. In the MMLU-Pro eval, the model even comes close to a Llama3 70B level of performance.

For 4k context length, try Phi-3 Medium 4K.

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 (8.0)

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

Create a Python file and import the necessary modules:

from marshmallow import fields, post_load, pre_load
from marshmallow.exceptions import ValidationError
from dataclasses import dataclass, field
from typing import List, Any
from decimal import Decimal
from datetime import date, datetime

Next, define the dataclass for Person:

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

Then, define the dataclass for Order:

@dataclass
class Order:
    created: datetime
    amount: Decimal

Next, create the schema to load data into the dataclasses:

class PersonSchema(ma.Schema):
    class Meta:
        ordered = True

    first_name = fields.String(required=True)
    last_name = fields.String(required=True)
    date_of_birth = fields.Date(required=True)
    orders = fields.Nested('OrderSchema', many=True, required=True)

    @pre_load
    def pre_load(self, data, **kwargs):
        data['date_of_birth'] = datetime.strptime(data['date_of_birth'], '%m/%d/%Y').date()
        data['orders'] = [Order(**item) for item in data['orders']]
        return data

    @post_load
    def make_object(self, data, **kwargs):
        return Person(**data)

    class Meta:
        ordered = True

Create the schema to validate data from dataclasses:

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

    @pre_load
    def pre_load(self, data, **kwargs):
        data['date_of_birth'] = date.fromisoformat(data['date_of_birth'])
        data['orders'] = [Order(**item) for item in data['orders']]
        return data

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

    @post_load
    def make_object(self, data, **kwargs):
        return Person(**data)

Now you can load and validate data using the schemas:

person_dict = {
    '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 = person_schema.load(person_dict)

print(person)

This will output:

Person(first_name='Jane', last_name='Smith', date_of_birth=datetime.date(1977, 3, 16), orders=[Order(created=datetime.datetime(2024, 1, 25, 15, 0, 4), amount=Decimal('100.00'))])

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) (1.07¢)

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 does not explicitly specify the date format for the date_of_birth field using format="%m/%d/%Y". Instead, it uses a pre_load method to parse the date. While this works, it doesn't match the exact requirement. (0 marks)
The decimal field does not include as_string=True. (0 marks)
The schema and dataclasses are linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution imports necessary modules. (1 mark)
It includes a pre_load method for additional data processing. (1 mark)

Breaking down the marks:

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

Final score:

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

Comments:
The solution provides a good overall structure for using marshmallow with dataclasses. It correctly defines the dataclasses and schema classes, and uses the @post_load decorator to link them. However, it misses two specific requirements: explicitly specifying the date format for date_of_birth and using as_string=True for the decimal field. The addition of pre_load methods for data processing is a good touch, although not specifically requested in the problem statement. With some minor adjustments to meet all the specified requirements, this solution could easily achieve a perfect score.

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

The given solution attempts to use the marshmallow library and dataclasses to deserialize JSON data into Python objects with proper types. Let's evaluate it according to the marking scheme:

Dataclass Definition (2 marks):
Person and Order dataclasses are well defined.
Both classes have correct type hints for their attributes.
Points awarded: 2/2
Schema Classes Definition (2 marks):
Two schema classes (PersonSchema and OrderSchema) are created.
However, there are multiple problems:
- The schema appears to be defined twice with one erroneous code block. Deduct points for the code quality and confusion.
Points awarded: 1/2
Date Formatting Specs (1 mark):
The date_of_birth in PersonSchema is handled correctly using a pre_load method to convert the string to a date object.
However, there's no use of a fields.Date with an explicit format attribute. Instead, explicit conversion is done in pre_load.
Points awarded: 0.5/1
Decimal Field Handling (1 mark):
The amount field as a Decimal is handled, but nowhere in the presented solutions is as_string=True used.
Instead, data handling directly converts string amount to Decimal.
Points awarded: 0.5/1
Post Load Method (2 marks):
@post_load is used in PersonSchema to return Person objects.
This is correctly implemented and crucial for integrating schema and dataclasses.
Points awarded: 2/2
Miscellaneous and Overall Code Quality (2 marks):
Code shows effort in integrating Marshmallow with dataclasses effectively.
However, there are repetitions, inconsistencies, and possible copy-paste errors in schema definitions. This can confuse users and lead to errors.
There is no error handling for incorrect data formats or validation failures.
Points awarded: 0.5/2

Overall, the solution attempts to adhere to the requirements but suffers from issues in clarity, repetition, and incomplete handling of specified requirements such as the formatting of Decimal fields and proper use of Marshmallow's type management features.

Final Score:

{
  "score": 6/10
}

Response 2 (1.0)

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

from marshmallow import fields

class Customer:
    class Meta:
        fields = ('first_name', 'last_name', 'date_of_birth')
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()

class Order:
    class Meta:
        fields = ('created', 'amount')
    created = fields.DateTime()
    amount = fields.Decimal()

To load the JSON data into these dataclasses, you can use the loads method from the load function in Marschmaallow library as shown below:

from marshmallow import load, Schema

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

schem = Customer()
output = load(data, schema=scheme, many=True, unknown='ignore')

print(output)

This will load your JSON data into the dataclasses for easy access and export.

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)

openai/gpt-4-0125-preview says: (0.0) ()

Evaluation of the Solution:

Dataclasses Definition:
The solution provided does not properly define Python dataclasses. Instead, it uses Marshmallow's fields incorrectly as class attributes, which is an incorrect implementation for dataclasses. Marshmallow fields should be used within Marshmallow schema classes to specify serialization/deserialization details, not as direct attributes of a business model class.
Score: 0/2
Schema Definition:
The solution fails to provide Marshmallow schema classes. Instead, it misuses fields within the class that seems to be intended as dataclasses. The correct approach is to define separate schema classes using Marshmallow's Schema, where such fields should be used.
Score: 0/2
Date Format Specification:
Date format for date_of_birth is not specified in the provided solution. The correct implementation should include format="%m/%d/%Y" in the respective Marshmallow field.
Score: 0/1
Decimal Field Specification:
The solution makes no indication of using as_string=True for the decimal field. This is crucial to specify in Marshmallow to ensure that decimals are handled as strings, thereby avoiding precision issues.
Score: 0/1
Post-load Linking:
The solution does not define or use any @post_load methods, which are essential for transforming loaded data into an instance of the defined dataclass. This misstep misses the interaction between data loaded through Marshmallow and the dataclass instances.
Score: 0/2
General JSON Loading Logic:
The loading logic provided is fundamentally flawed and incorrect:
The load method is incorrectly called (it should be loads from a schema object, not load).
There is a typo in variable naming (scheme should be schem).
The use of the many=True argument is misplaced as the JSON provided suggests a single customer instance.
The dataclasses module is entirely unused, but it should have been leveraged to correctly define data models.
Score: 0/2

Conclusion:
Overall, the provided solution shows a series of misunderstanding both in the usage of the Marshmallow library and in the fundamental Python practices regarding dataclasses and schema. The solution does not achieve the basic requirements of the task.

Final Score:

{
  "score": 0
}

anthropic/claude-3.5-sonnet says: (1.0) (0.99¢)

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

The solution defines two classes (Customer and Order), but they are not proper dataclasses. They seem to be a mix of Marshmallow schemas and dataclasses. (0/2 marks)
The solution doesn't explicitly define separate schema classes. The Customer and Order classes are attempting to serve as both dataclasses and schemas, which is incorrect. (0/2 marks)
The solution doesn't specify the date format for the date_of_birth field. (0/1 mark)
The solution doesn't include as_string=True for the decimal field. (0/1 mark)
The solution doesn't include @post_load methods to link schemas and dataclasses. (0/2 marks)
Other observations:
The solution attempts to use Marshmallow, but the implementation is incorrect.
The use of load function is incorrect; Marshmallow uses Schema.load() method.
The Meta class usage is incorrect for Marshmallow schemas.
The solution doesn't handle nested orders correctly.
(1/2 marks for attempting to use Marshmallow, albeit incorrectly)

Here's the breakdown of marks:

{
  "Dataclass Definition": 0,
  "Schema Definition": 0,
  "Date Format Specification": 0,
  "Decimal Field Specification": 0,
  "Post_load Method": 0,
  "Other Observations": 1
}

Final score:

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

The solution shows an attempt to use Marshmallow, but it has significant misunderstandings about how to properly implement dataclasses and Marshmallow schemas. It doesn't correctly address any of the specific requirements mentioned in the problem statement, such as proper date formatting or handling of decimal fields. The approach to loading data is also incorrect. The solution would need substantial revisions to correctly solve the given problem using Marshmallow and dataclasses.

Response 3 (4.0)

You can define your classes using the dataclasses module in Python. Here's an example of how to do this with the given json data:

from dataclasses import dataclass
from datetime import datetime
from decimal import Decimal

@dataclass
class Order:
    created: datetime
    amount: Decimal

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

In this code, we define two dataclasses: Order and Person. The Order class has created as a datetime object, and amount as a Decimal. The Person class has first_name and last_name as str, and date_of_birth as a datetime object. It also has orders as a list of Order objects.

You can create an instance of the Person class and fill the attributes with the values from the given json data like this:

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

This creates a Person object with the given 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: (4.0) (0.91¢)

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

The solution correctly defines two dataclasses, Order and Person. (2 marks)
The solution does not define any schema classes using marshmallow. (0 marks)
The solution does not use marshmallow to specify the date format for the date_of_birth field. However, it does use the correct format in the datetime.strptime() method. (0 marks)
The solution does not use marshmallow's Decimal field with as_string=True. It uses Python's built-in Decimal class instead. (0 marks)
There are no schema classes, so there are no @post_load methods linking schemas and dataclasses. (0 marks)
Additional points:
The solution correctly imports necessary modules (datetime, decimal). (1 mark)
The solution demonstrates how to create an instance of the Person class with the given JSON data, which is helpful. (1 mark)

Comments:
The solution provides a good starting point with dataclasses, but it doesn't use the marshmallow library as requested in the problem statement. It lacks schema definitions and the specific marshmallow field configurations mentioned in the marking scheme. While the dataclass definitions are correct, the solution doesn't address the serialization and deserialization aspects that marshmallow would provide.

Here's the breakdown of marks:

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

Final score:

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

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

The provided solution partially fulfills the requirements indicated by the problem statement and the marking scheme but contains several inaccuracies and omissions.

Defining two dataclasses (2 marks): The solution defines the two required dataclasses, Order and Person, effectively fulfilling this condition.
Defining two schema classes (0 marks): The solution does not define any schema classes using the marshmallow library, which is essential for serialization and deserialization of the data. This is a missing part of the solution.
Specify the date format for the date_of_book field (0 marks): There is no implementation or reference to a schema using marshmallow that would allow the specification of the date format (%m/%d/%Y) for the date_of_birth field. This requirement is not met.
Decimal field needs as_string=True (0 marks): Again, since there is no schema defined using marsharshal, the requirement to specify as_string=True for the Decimal field is not met.
Link schema and dataclasses with a @post_load method (0 marks): The absence of marshmallow schema classes means that there's also no @post_load method used to link dataclass instantiation with deserialization. This is a critical omission that hinders data processing using the marshmallow framework.
Anything else (1 mark): The solution effectively demonstrates how to manually create an instance of the respective dataclasses with appropriate type parsing for date and decimal types, which fulfills part of the practical usage needed from the data structure definition, albeit not using marshmallow.

Given the scope of the assignment related to using marshmallow specifically, the solution's omission of any actual marshmallow implementation is significant.

{
  "score": 3
}

1.00	0.76	0.61
0.76	1.00	0.61
0.61	0.61	1.00

Phi-3 Medium 128K Instruct

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