[Feature Research]: Lepton AI

[abdulsamodazeez]

Feature Name

Lepton AI

Feature Description

Research about Lepton AI

Research Findings

Lepton AI

Lepton AI is a cutting-edge provider of AI infrastructure and services, designed to streamline the deployment and scaling of AI models. They offer a high-performance computing environment with top-tier GPU infrastructure, ensuring fast and efficient AI inference and training. Their cloud-native platform guarantees 99.9% uptime with comprehensive health checks and automatic repairs, making it a reliable choice for enterprises.

Lepton AI is known for its Pythonic framework, which simplifies the process of converting research and modeling code into production-ready services. They cater to a wide range of use cases, including image generation, natural language processing, and large-scale data processing. With features like dynamic batching, quantization, and speculative decoding, Lepton AI provides a robust and scalable solution for businesses looking to leverage AI technology. Their commitment to security and compliance, including SOC2 and HIPAA certifications, makes them a trusted partner for enterprise applications.

Use Cases

• AI Model Deployment: Easily deploy AI models in a production-ready environment without needing to learn containers or Kubernetes.
• Image Generation: Supports high-resolution image generation with distributed engines.
• Natural Language Processing: Efficiently run NLP models like GPT-2 and Llama2.
• Data Processing: Handle large-scale data processing tasks with accelerated serverless storage.

Additional Information

• Scalability: Lepton AI supports scaling from small projects to large enterprise applications.
• Ease of Use: With simple abstractions and prebuilt examples, it’s easy to get started and deploy models quickly.
• Community and Support: Lepton AI has a strong community and offers extensive documentation and support to help you get the most out of their platform.

Links

• Lepton AI Homepage
• Lepton AI Documentation
• Lepton AI References
• Lepton AI GitHub
• Lepton AI Login Portal

Potential Impact

Lepton AI has the potential to significantly transform various industries by making AI more accessible and efficient. By providing a high-performance computing environment with top-tier GPU infrastructure, Lepton AI enables faster and more efficient AI model training and inference. This can lead to quicker deployment of AI solutions, reducing time-to-market for innovative products and services.

In sectors like healthcare, finance, and retail, Lepton AI’s robust and scalable platform can enhance data processing capabilities, improve predictive analytics, and enable more personalized customer experiences. For instance, in healthcare, it can support advanced diagnostic tools and personalised treatment plans. In finance, it can enhance fraud detection and risk management. Overall, Lepton AI’s comprehensive and secure AI infrastructure can drive significant advancements in technology and business processes, fostering innovation and efficiency across various domains.

Additional Resources (optional)

No response

Feature Priority

High

[DevDigiLance]

No test cases

[cobycloud]

@DevDigiLance

import pytest
import os
from swarmauri.standard.llms.concrete.LeptonAIModel import LeptonAIModel as LLM
from swarmauri.standard.conversations.concrete.Conversation import Conversation
from swarmauri.standard.messages.concrete.AgentMessage import AgentMessage
from swarmauri.standard.messages.concrete.HumanMessage import HumanMessage
from swarmauri.standard.messages.concrete.SystemMessage import SystemMessage

@pytest.mark.skipif(not os.getenv('LEPTON_API_KEY'), reason="Skipping due to environment variable not set")
@pytest.mark.unit
def test_ubc_resource():
    API_KEY = os.getenv('LEPTON_API_KEY')
    llm = LLM(api_key = API_KEY)
    assert llm.resource == 'LLM'

@pytest.mark.skipif(not os.getenv('LEPTON_API_KEY'), reason="Skipping due to environment variable not set")
@pytest.mark.unit
def test_ubc_type():
    API_KEY = os.getenv('LEPTON_API_KEY')
    llm = LLM(api_key = API_KEY)
    assert llm.type == 'LeptonAIModel'

@pytest.mark.skipif(not os.getenv('LEPTON_API_KEY'), reason="Skipping due to environment variable not set")
@pytest.mark.unit
def test_serialization():
    API_KEY = os.getenv('LEPTON_API_KEY')
    llm = LLM(api_key = API_KEY)
    assert llm.id == LLM.model_validate_json(llm.model_dump_json()).id

@pytest.mark.skipif(not os.getenv('LEPTON_API_KEY'), reason="Skipping due to environment variable not set")
@pytest.mark.unit
def test_default_name():
    API_KEY = os.getenv('LEPTON_API_KEY')
    model = LLM(api_key = API_KEY)
    assert model.name == 'llama3-8b'

@pytest.mark.skipif(not os.getenv('LEPTON_API_KEY'), reason="Skipping due to environment variable not set")
@pytest.mark.unit
def test_no_system_context():
    API_KEY = os.getenv('LEPTON_API_KEY')
    model = LLM(api_key = API_KEY)
    conversation = Conversation()

    input_data = "Hello"
    human_message = HumanMessage(content=input_data)
    conversation.add_message(human_message)

    model.predict(conversation=conversation)
    prediction = conversation.get_last().content
    assert type(prediction) == str

@pytest.mark.skipif(not os.getenv('LEPTON_API_KEY'), reason="Skipping due to environment variable not set")
@pytest.mark.acceptance
def test_nonpreamble_system_context():
    API_KEY = os.getenv('LEPTON_API_KEY')
    model = LLM(api_key = API_KEY)
    conversation = Conversation()

    # Say hi
    input_data = "Hi"
    human_message = HumanMessage(content=input_data)
    conversation.add_message(human_message)

    # Get Prediction
    model.predict(conversation=conversation)

    # Give System Context
    system_context = 'You only respond with the following phrase, "Jeff"'
    human_message = SystemMessage(content=system_context)
    conversation.add_message(human_message)

    # Prompt
    input_data = "Hello Again."
    human_message = HumanMessage(content=input_data)
    conversation.add_message(human_message)

    model.predict(conversation=conversation)
    prediction = conversation.get_last().content
    assert 'Jeff' in prediction

@pytest.mark.skipif(not os.getenv('LEPTON_API_KEY'), reason="Skipping due to environment variable not set")
@pytest.mark.unit
def test_preamble_system_context():
    API_KEY = os.getenv('LEPTON_API_KEY')
    model = LLM(api_key = API_KEY)
    conversation = Conversation()

    system_context = 'You only respond with the following phrase, "Jeff"'
    human_message = SystemMessage(content=system_context)
    conversation.add_message(human_message)

    input_data = "Hi"
    human_message = HumanMessage(content=input_data)
    conversation.add_message(human_message)

    model.predict(conversation=conversation)
    prediction = conversation.get_last().content
    assert type(prediction) == str
    assert 'Jeff' in prediction

@pytest.mark.skipif(not os.getenv('LEPTON_API_KEY'), reason="Skipping due to environment variable not set")
@pytest.mark.acceptance
def test_multiple_system_contexts():
    API_KEY = os.getenv('LEPTON_API_KEY')
    model = LLM(api_key = API_KEY)
    conversation = Conversation()

    system_context = 'You only respond with the following phrase, "Jeff"'
    human_message = SystemMessage(content=system_context)
    conversation.add_message(human_message)

    input_data = "Hi"
    human_message = HumanMessage(content=input_data)
    conversation.add_message(human_message)

    prediction = model.predict(conversation=conversation)

    system_context_2 = 'You only respond with the following phrase, "Ben"'
    human_message = SystemMessage(content=system_context_2)
    conversation.add_message(human_message)

    input_data_2 = "Hey"
    human_message = HumanMessage(content=input_data_2)
    conversation.add_message(human_message)

    model.predict(conversation=conversation)
    prediction = conversation.get_last().content
    assert type(prediction) == str
    assert 'Ben' in prediction