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Python library

Lamini is a Python package designed to build Large Language Models (LLMs) for natural language processing tasks. It provides an engine for creating and running your own LLMs. With Lamini, you can train language models on large text corpora and improve them following your guidelines, which can then be used for generating and extracting text.

Lamini is the tribe of which llamas are a part. You will be importing the llama module in order to work with the LLM engine.

Input and output types

First, you want to construct some data types: (1) input types as arguments into the LLM and (2) output types as return values from the LLM.

You can use the Type and Context classes in the library create them.

For example, you can create an Animal type as follows:

from llama import Type, Context

class Animal(Type):
    name: str = Context("name of the animal")
    n_legs: int = Context("number of legs that animal has")

llama_animal = Animal(name="Larry", n_legs=4)

Each Type requires at least one attribute, such as name and n_legs here. They can be anything you would like. Be sure to add a Context field to each attribute, with a natural language description of the attribute. That is required to tell the model what you mean by each attribute.

Note: to play with different types in a user interface, you can log in at https://lamini.ai and find a playground to run LLMs with different types.

Running the LLM

Next, you want to instantiate your LLM engine with LLM.

llm = LLM(name="animal_stories")

# If you want to use a different base model or add your config options here
llm = LLM(
    name="my_llm_name",
    model_name="chat-gpt",
    config={
        "production": {
            "key": "<API-KEY-HERE>",
        }
    },
)

Now, you can now run your LLM.

# Define an output type
class Story(Type):
    story: str = Context("Story of an animal")

llama_animal = Animal(name="Larry", n_legs=4)
llama_story = llm(llama_animal, output_type=Story)

Adding data

You have data on different inputs and outputs, and in some cases, you have pairs of inputs and outputs that you want the LLM to model after.

Getting data of good inputs:

llama_animal = Animal(name="Larry", n_legs=4)
centipede_animal = Animal(name="Cici", n_legs=100)

my_data = [llama_animal, centipede_animal]

Getting data of a good input-output pair:

dog_animal = Animal(name="Nacho", n_legs=4)
dog_speed = Story(story="There once was a cute doggo named Nacho. She was a golden retriever who liked to run. All four of her paws were adorable.")

my_data.append([dog_animal, dog_speed])

Now add all that data to your LLM:

llm.add_data(my_data)

With the same call to the LLM engine, it should now produce a story that is more aligned with your data.

llama_story = llm(llama_animal, output_type=Story)

Improving with criteria

Now that you've added data, you want to improve the model's outputs further. Another way to do that is to supply improve statements on different attributes of a model's output type to improve on. You can use natural language to tell the model how it should improve.

llm.improve(on="story", to="specify the number of legs in a subtle way")

Full example

Start with data.

class Animal(Type):
    name: str = Context("name of the animal")
    n_legs: int = Context("number of legs that animal has")

class Speed(Type):
    speed: float = Context("how fast something can run")

llama_animal = Animal(name="Larry", n_legs=4)
centipede_animal = Animal(name="Cici", n_legs=100)

my_data = [llama_animal, centipede_animal]

dog_animal = Animal(name="Nacho", n_legs=4)
dog_speed = Story(story="There once was a cute doggo named Nacho. She was a golden retriever who liked to run. All four of her paws were adorable.")

my_data.append([dog_animal, dog_speed])

Instantiate the LLM engine, add data, add improvements (as many as you like), and run the LLM engine.

llm = LLM(name="animal_stories")

llm.add_data(my_data)
llm.improve(on="story", to="specify the number of legs in a subtle way")

story = llm(llama_animal, output_type=Story)

A common workflow is to run the LLM engine and see issues in the LLM outputs, then add an improve statement and run the LLM engine again.