Goliath 120B

Description

A large LLM created by combining two fine-tuned Llama 70B models into one 120B model. Combines Xwin and Euryale.

Credits to
- @chargoddard for developing the framework used to merge the model - mergekit.
- @Undi95 for helping with the merge ratios.

#merge

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 (1.0)

To achieve this using the marshmallow library in Python, you can first install the library with pip:

pip install marshmallow

Then, you can create a new Python file (e.g., app.py) and add the following code:
``python import marshmallow as ma import marshmallow_@datetime import Timestamp
from marshmallow_ @decimal import DecimalapedNu

from datetime import date
from typing import Dict, Union

class Order(ma. principales):
created: Union[Timestamp, str]
amount: Union[Decimal, str]

def mote(self, value: dict) -> Union[dict, Timestamp]:
    return Timestamp.from_isoformat(value['created']) if isinstance(value, dict) else value

class User(ma. Schema):
first_name: str
last_name: str
date_of_birth: Union[date, str]
orders: Union[list[Order], list[Any]]

def mote(self, value: dict) -> User:
    date_of_birth_parsed = None
    if 'date of_birth' in value and value['date_of_birth']:
        date_of_birth_parsed: date = datetime.fromisoformat(value['date_of_birth'].split( )[0])
    orders = [order_schema(order) for order in value.get('orders', [])] if 'orders' in value else No

    return User(**value, date_of_birth=date_of_birth_parased, orders=orders)

def main():

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: (1.0) (0.96¢)

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

Defining two dataclasses (2 marks):
The solution doesn't use dataclasses. It defines two classes (Order and User) but they're not proper dataclasses.
Defining two schema classes (2 marks):
The solution doesn't properly define schema classes. It's using ma.Schema incorrectly for the User class, and there's no schema defined for Order.
Specifying date format for date_of_birth (1 mark):
The solution doesn't specify the required date format ("%m/%d/%Y") for date_of_birth.
Decimal field with as_string=True (1 mark):
The solution doesn't use a Decimal field with as_string=True for the amount.
Linking schema and dataclasses with @post_load (2 marks):
The solution doesn't use @post_load to link schema and dataclasses.
Anything else (2 marks):
The solution attempts to handle datetime parsing for 'created' field, which is good.
It tries to handle the nested structure of orders within User.
However, there are several syntax errors and incorrect use of marshmallow.

Here's the breakdown of marks:

{
  "Defining dataclasses": 0,
  "Defining schema classes": 0,
  "Date format specification": 0,
  "Decimal field configuration": 0,
  "Linking schema and dataclasses": 0,
  "Anything else": 1
}

Final score:

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

The solution provided doesn't correctly implement the requirements using the marshmallow library. It mixes concepts from different libraries and contains syntax errors. The approach shows some understanding of the problem, but fails to correctly use marshmallow for schema definition and data validation/deserialization.

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

This solution attempt, claiming to use Marshmallow for data serialization, shows significant misunderstandings and incorrect implementations regarding Marshmallow's capabilities and typical usage patterns. Here is the evaluation based on the provided criteria:

Dataclasses Definition: The solution attempts to create classes Order and User, but incorrectly labels them as ma.principales and ma.Schema, respectively. These are not actual constructs in Marshmallow or Python's standard library for defining dataclasses; Marshmallow uses Schemas for serialization, not base class definition. (0/2)
Schema Classes Definition: The classes Order and User are intended to be schema classes but are not correctly implemented as Marshmallow schema classes. For example, ma.Schema is the correct base class for defining a Marshmallow schema, but the fields are not defined using Marshmallow field types. (0/2)
Date Format Specification: The solution does not specify the formatting of the date_of_birth field using Marshmallow’s fields with the appropriate format parameter. The attempt to parse the date (incorrectly split and imported) within a method does not meet the requirement of using Marshmallow’s built-in date formatting capabilities. (0/1)
Decimal Field Specification: There’s no proper use of Marshmallow's Decimal field with as_string=True. Instead, classes are creating hybrid types with Python’s Decimal, which is not the correct approach withing Marshmallow context. (0/1)
@post_load Method Linking: The methods label mote within the classes appear to be intended for data processing after loading but are not correctly implemented Marshmallow @post_load methods. The methods are improperly formulated and executed, neither utilizing @post_load decorator nor structuring and returning data appropriately. (0/2)
Additional Elements:
Incorrect import and usage of Python and Marshmallow modules and attributes (e.g., non-existent ma.principales).
Misunderstanding of how data should be structured and deserialized in a typical Marshmallow pattern, such as managing list comprehensions and attribute assignments.
Syntactical and logical errors throughout the code that prevent it from being operational or meeting the intended goals. (0/2)

Evaluating all these aspects results in a score of:

{
  "score": 0
}

This assessment reflects major foundational issues in both Marshmallow usage and general Python coding practices, suggesting a need for substantial revisions and corrections.

Response 2 (1.0)

You can use the marshmallow library with dataclasses to deserialize JSON data into Python objects while still maintaining type safety. First, you'll need to install the marshmallow-dataclassschema package and dataclasses itself if you haven't already:

pip3 install marshmallow[dataclass]
pip3 install marshmallow-dataclassschema

Then, you can create a new file, let's say data_classes.py, and define the dataclasses and the marshmallow fields as follows:

from dataclasses import dataclass
import datetime
from typing import Optional
from marshmallow import Schema, fields, post_load, deserialize
from marshmallow_dataclass import dataclass_schema
from marshmallow_dataclass import dataclass_schematype
from marshmallow_dataclassschema import field_getter
import decimal
from typing_extensions import Literal
from typing import Union
import typing
from typing import Dict, Any
import typing as tp
from typing import Union
import typing_util

from datetime import datetime
from typing_x import UnionConstrainedraising
from typing_extensions import Literal

from typing_util import raise_from
from typing_util import force
from typing_x import UnionFromWrappedUnion
from typing_x.util.mypy_extensions import

```python
from marshmallow import Schema, fields, post_load, post_dump
from marshmallow_dataclass import dataclass_schema
from marshmallow_dataclass import dataclass_schema_extended
from marshmallow_dataclass import dataclass_schematype
from marimba_dataclassschema import (field_get

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: (1.0) (0.93¢)

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

The solution does not clearly define two dataclasses for the person and the order. (0/2 marks)
The solution does not explicitly define two schema classes for the person and the order. (0/2 marks)
The solution does not specify the date format for the date_of_birth field. (0/1 mark)
The solution does not mention as_string=True for the decimal field. (0/1 mark)
The solution does not show a @post_load method linking the schema and dataclasses. (0/2 marks)
The solution does import necessary libraries and modules, which is a step in the right direction. However, it's incomplete and includes some unnecessary or non-existent imports. (1/2 marks)

Comments:
The provided solution is incomplete and does not fully address the requirements. It mainly focuses on importing libraries, some of which are not standard or may not exist (e.g., typing_x, typing_util). The solution doesn't show the actual implementation of dataclasses or schema classes, nor does it demonstrate how to handle the specific field types (date, datetime, decimal) as requested in the problem statement.

To improve, the solution should define the actual dataclasses for Person and Order, create corresponding schema classes using marshmallow, and properly handle the specific field types with the required formats and options.

Here's the breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 0,
  "Date format specification": 0,
  "Decimal field handling": 0,
  "Post_load method": 0,
  "Other aspects": 1
}

Final score:

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

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

The provided solution, which intended to demonstrate the use of the marshmallow library along with Python dataclasses to deserialize JSON data, made some mistakes and omissions based on the requirements given. Here is the analysis based on the marking scheme:

Dataclass Definition:
The solution does not define any dataclasses directly in the provided code.
Score: 0/2
Schema Class Definition:
Similarly, no schema classes are explicitly defined in the provided code.
Score: 0/2
Date Format Specification:
No fields specifying date format, especially for date_of_birth with the specified format %m/%d/%Y, are present in the provided code.
Score: 0/1
Decimal Field as String:
There is no mention or use of the Decimal field with as_string=True for the amount field.
Score: 0/1
Link Between Schema and Dataclasses through @post_load:
There is no implementation or demonstration of the @post_load method to connect schema parsing results to dataclasses.
Score: 0/2
Others:
The provided solution contains a significant amount of unnecessary imports.
There is confusion and redundancy in import statements with many unused or unrelated Python libraries.
The actual implementation that would parse and convert the JSON according to the requirements is entirely missing.
There appears to be a misunderstanding or miscommunication regarding the appropriate usage of packages such as marshmallow-dataclass.
Score: 0/2

Overall, the solution fails to meet the provided specifications for implementing marshmallow with dataclasses to handle specific data field types from JSON input correctly. The imports are incorrectly managed, and the expected code structure is absent.

Total Score:

{
  "score": 0
}

Response 3 (5.0)

To achieve this using the Marshmallow library in Python, you can define the following dataclasses:

Order:

import datetime
from marshmallow import Schema, fields, post_load,.
from typing import Tuple
from typing_extensions import TypedDict
from dateutil.parser import parse
from decimal import Decimal


class Order(Schema):
    created: datetime.datetime = fields.DateTime(input_format='%Y-%m-%dT%H:%M:%S')
    amount: Decimal = fields.Decimal(decimal_places=2)


    @post_load
    def post_load(self, data: TypedDict, many: bool, **kwargs):
        if many:
            return [Order(**o) for o in data]
        return Order(**data)

Person:
```python
from marshmallow import Schema, fields, post_load, pre_dump, pre_load
from typing_extensions import TypedDict
from datetime import date
from typing import Dict, Union
from dateutil.parser import parse
from decimal import Decimal

class Person(Schema):
class Order(Schema):
created: datetime.datetime = fields.DateTime(input_format='%Y-%m-%dT%H:%M:%S')
amount: Decimal = fields.Decimal(decimal_places=2)

first_name: str = fields.Str()
last_name: str = fields.Str()
date_of_birth: date = fields.Date(input_format='%m/%d/%Y')
orders: Union[list[Order], Order, dict, dict] =

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: (1.0) ()

Evaluating the provided solution, let's review how well it aligns with the grading criteria:

Definition of two dataclasses: The solution does not define any data classes. Order and Person are defined as Marshmallow Schema classes, but not as Python data classes. The solution should instead use dataclasses.dataclass for defining Order and Person.
Definition of two schema classes: The schema classes are present (Order, Person), but they are incorrectly defined as inheriting from Schema when they should be separate from the actual schemas for serialization.
Date format specification for date_of_birth: Although mentioned in Person schema, the attribute uses the wrong parameter input_format instead of format. This partially meets the requirement.
Decimal field uses as_string=True: This attribute is missing in the amount field definitions in both the Order implementation inside Person and the top-level Order schema, failing to meet the required specification.
Linking schema and dataclasses via @post_load method: A @post_load method is present in both schema definitions, but they concern creating instances of Schema instead of actual dataclass objects. The data-related manipulation should instantiate actual data classes.
Additional issues:
- A syntax error with an extraneous period after import.
- The solution should implement explicit data classes using Python's dataclasses module.
- It's incorrect and unnecessary to nest the Order schema inside Person.

Score Calculation:

Data class definitions: 0 / 2 (No proper dataclass definitions)
Schema class definitions: 0 / 2 (Incorrect use of Schema instead of a separate schema class for serialization)
Date format: 0.5 / 1 (Improper usage of parameter)
Decimal string conversion: 0 / 1 (Missing as_string=True)
Post-load linkage: 0 / 2 (Incorrectly instantiating Schema objects)
Additional aspects: 0.5 / 2 (Many other structural and syntax errors)

{
  "score": 1
}

anthropic/claude-3.5-sonnet says: (5.0) (1.15¢)

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

Defining two dataclasses (2 marks):
The solution doesn't define dataclasses. Instead, it defines Schema classes. 0/2 marks.
Defining two schema classes (2 marks):
The solution defines two schema classes: Order and Person. 2/2 marks.
Specifying date format for date_of_birth (1 mark):
The solution correctly specifies the date format for date_of_birth as input_format='%m/%d/%Y'. 1/1 mark.
Decimal field with as_string=True (1 mark):
The solution doesn't include as_string=True for the decimal field. 0/1 mark.
Linking schema and dataclasses with @post_load (2 marks):
The solution includes a @post_load decorator for the Order schema, but it's not correctly implemented and there's no @post_load for the Person schema. 1/2 marks.
Anything else (2 marks):
The solution attempts to handle both single and multiple orders in the Order schema's post_load method.
It imports necessary libraries and uses type hints.
However, there are some syntax errors and incomplete implementations.
1/2 marks.

Comments:
The solution shows an understanding of Marshmallow schemas but lacks the required dataclass definitions. There are several issues:
- The Order class is defined twice, once as a standalone schema and once nested in the Person schema.
- The Person schema is incomplete and has syntax errors.
- The use of TypedDict seems unnecessary and is not correctly implemented.
- The solution doesn't properly separate the schema definitions from the data models.

Here's the breakdown of marks:

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

Final score:

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

The solution scores 5 out of 10 points. It demonstrates some understanding of Marshmallow schemas but fails to meet several key requirements and contains errors that would prevent it from functioning correctly.

1.00	0.66	0.79
0.66	1.00	0.74
0.79	0.74	1.00

Goliath 120B

Description

Score Calculation:

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