NVIDIA Nemotron-4 340B Instruct

Description

Nemotron-4-340B-Instruct is an English-language chat model optimized for synthetic data generation. This large language model (LLM) is a fine-tuned version of Nemotron-4-340B-Base, designed for single and multi-turn chat use-cases with a 4,096 token context length.

The base model was pre-trained on 9 trillion tokens from diverse English texts, 50+ natural languages, and 40+ coding languages. The instruct model underwent additional alignment steps:

Supervised Fine-tuning (SFT)
Direct Preference Optimization (DPO)
Reward-aware Preference Optimization (RPO)

The alignment process used approximately 20K human-annotated samples, while 98% of the data for fine-tuning was synthetically generated. Detailed information about the synthetic data generation pipeline is available in the technical report.

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 (6.0)

To generate dataclasses from JSON using the marshmallow library, you can follow these steps:

Install the required libraries: marshmallow and marshmallow-dataclass. You can install them using pip:

pip install marshmallow marshmallow-dataclass

Create a schema for your data using marshmallow's Schema and fields. Here's an example based on your JSON:

from datetime import date, datetime
from decimal import Decimal
from marshmallow import Schema, fields, validate
from marshmallow_dataclass import dataclass

@dataclass
class Order:
    created: datetime
    amount: Decimal

class OrderSchema(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(required=True, validate=validate.Range(min=0))

@dataclass
class User:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: list[Order]

class UserSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(required=True)
    orders = fields.List(fields.Nested(OrderSchema), required=True)

Now you can use the UserSchema to load JSON data into a User dataclass:

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

user_schema = UserSchema()
user = user_schema.load(json_data)
print(user)

This will create a User dataclass instance with the correct data types for each field.

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) (1.02¢)

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

Defining two dataclasses (Person and Order):
The solution correctly defines two dataclasses: User and Order.

{"marks 1": 2}

Defining two schema classes:
The solution correctly defines two schema classes: UserSchema and OrderSchema.

{"marks 2": 2}

Specifying the date format for date_of_birth:
The solution does not specify the date format for the date_of_birth field. It should have used fields.Date(format="%m/%d/%Y").

{"marks 3": 0}

Using as_string=True for the decimal field:
The solution does not use as_string=True for the amount field in OrderSchema.

{"marks 4": 0}

Linking schema and dataclasses with @post_load:
The solution does not use @post_load to link the schema and dataclasses.

{"marks 5": 0}

Additional considerations:
The solution uses marshmallow-dataclass, which simplifies the process of creating dataclasses.
It includes validation for the amount field (must be non-negative).
It properly imports necessary modules and suggests installing required libraries.

{"marks 6": 2}

Comments:
The solution provides a good starting point but misses some important details. It doesn't specify the date format for date_of_birth, which could lead to parsing errors. The decimal field is not set to use strings, which might cause issues with precision. The @post_load decorator is not used, which means the schemas won't automatically create dataclass instances. However, the use of marshmallow-dataclass partially mitigates this issue.

Score breakdown:

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

Response 2 (5.0)

To generate dataclasses with the marshmallow library in Python, you can use the marshmallow-dataclass package. Here's how you can define the dataclasses for your JSON data:

First, install the required packages:

pip install marshmallow marshmallow-dataclass

Now, you can create the dataclasses:

from datetime import date, datetime
from decimal import Decimal
from marshmallow import fields
from marshmallow_dataclass import dataclass

@dataclass
class Order:
    created: datetime = fields.DateTime(data_key="created")
    amount: Decimal = fields.Decimal(data_key="amount")

@dataclass
class User:
    first_name: str = fields.Str(data_key="first_name")
    last_name: str = fields.Str(data_key="last_name")
    date_of_birth: date = fields.Date(data_key="date_of_birth")
    orders: list[Order] = fields.List(fields.Nested(Order), data_key="orders")

To load the JSON data into the dataclasses, you can use the Schema class provided by marshmallow:

from marshmallow import Schema

class UserSchema(Schema):
    user = fields.Nested(User)

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

user_schema = UserSchema()
user = user_schema.load(json_data)["user"]

Now, user is an instance of the User dataclass with the correct data types.

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) (1.01¢)

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

The solution defines two dataclasses, Order and User. (2 marks)
The solution defines one schema class, UserSchema. It's missing a separate schema for Order. (1 mark)
The solution doesn't specify the date format for the date_of_birth field. It should have included format="%m/%d/%Y". (0 marks)
The decimal field doesn't include as_string=True. (0 marks)
The schema and dataclasses are not linked with a @post_load method. (0 marks)
Additional points:
The solution uses marshmallow-dataclass, which simplifies the process.
It correctly imports necessary modules and sets up the structure.
It provides an example of how to use the schema to load JSON data.
(2 marks)

Here's the breakdown of the marks:

{
  "Dataclass definition": 2,
  "Schema definition": 1,
  "Date format specification": 0,
  "Decimal field specification": 0,
  "Schema-dataclass linking": 0,
  "Additional points": 2
}

Final score:

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

Comments:
The solution provides a good start by using marshmallow-dataclass, which simplifies the process of creating dataclasses with marshmallow fields. However, it misses several key requirements:

It doesn't create a separate schema for the Order class.
It doesn't specify the correct date format for date_of_birth.
The decimal field is missing the as_string=True parameter.
There's no @post_load method to link the schema and dataclasses.

To improve, the solution should address these missing elements and provide more detailed schema definitions that match the specific requirements of the problem.

Response 3 (7.0)

To create dataclasses with the required types using the marshmallow library, you can follow these steps:

Install the marshmallow and marshmallow-dataclass libraries if you haven't already:

pip install marshmallow marshmallow-dataclass

Create a schema for the Order class:

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

@dataclass
class Order:
    created: datetime.datetime
    amount: Decimal

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

Create a schema for the main class, which includes the date_of_birth field:

from marshmallow import validate

@dataclass
class User:
    first_name: str
    last_name: str
    date_of_birth: datetime.date
    orders: List[Order]

class UserSchema(Schema):
    first_name = fields.Str(required=True)
    last_name = fields.Str(required=True)
    date_of_birth = fields.Date(format="%m/%d/%Y", validate=validate.Length(equal=10))
    orders = fields.List(fields.Nested(OrderSchema))

Now you can use the UserSchema to load the JSON data into a dataclass:

import 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"
    }
  ]
}
"""

user_data = json.loads(json_data)
user_schema = UserSchema()
user = user_schema.load(user_data)

Now the user variable contains a User dataclass instance with the correct types for date_of_birth, created, and 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: (7.0) (1.01¢)

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

The solution defines two dataclasses, Order and User. (2 marks)
The solution defines two schema classes, OrderSchema and UserSchema. (2 marks)
The solution specifies the date format for the date_of_birth field using format="%m/%d/%Y". (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 libraries.
It uses marshmallow-dataclass for creating dataclasses.
It includes a validation for the date_of_birth field.
It provides an example of how to use the schema to load JSON data.
(2 marks)

Breakdown of marks:

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

Score:

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

The solution provides a good start but misses a couple of important points. It correctly defines the dataclasses and schema classes, and properly handles the date format for date_of_birth. However, it doesn't use as_string=True for the decimal field, which is important for handling decimal values correctly. Additionally, it doesn't use the @post_load decorator to link the schema and dataclasses, which would ensure that the loaded data is converted into the appropriate dataclass instances. Despite these omissions, the solution demonstrates a good understanding of using marshmallow with dataclasses and provides additional helpful information.

1.00	0.95	0.93
0.95	1.00	0.92
0.93	0.92	1.00

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