monsterapi 1.0.9.3

A Python client for Monster API v2

Monsterapi v2

A Python client for interacting with Monster API v2 in .

Installation

pip install monsterapi

Note: For detailed documentation please visit here

Has support to following MonsterAPI services:

Beta Next Gen LLM Services

Supported Models:

1. "TinyLlama/TinyLlama-1.1B-Chat-v1.0", 
2. "microsoft/phi-2"
3. "mistralai/Mistral-7B-Instruct-v0.2" 
4. "HuggingFaceH4/zephyr-7b-beta" 

Highlights:

1. 99% Cheaper 
2. Synchronous results
3. Token based Pricing. 

Service Introduction: here API Reference: here Usage Guide: here

GA LLM Models Old Generation

Supported Models:

1. falcon-7b-instruct
2. mpt-7b-instruct
3. llama2-7b-chat
4. falcon-40b-instruct
5. mpt-30b-instruct
6. codellama-13b-instruct
7. zephyr-7b-beta
8. Monster Deploy LLMs (deploy-llm)

Detailed payloads of models that are supported ? here

2. QuickServe API: New service from monsterapi deploy popular LLM models into monsterapi compute infrastructure with one request.

   a. How to use client to launch and manage a quickserve deployment ? here

Additional Information link: here

Code Documentation:

Client module code documentation can be found here

Basic Usage to access Hosted AI-Models

Import Module

from monsterapi import client

set MONSTER_API_KEY env variable to your API key.

os.environ["MONSTER_API_KEY"] = <your_api_key>
client = client() # Initialize client

or

pass api_key parameter to client constructor.

client = client(<api_key>) # pass api_key as parameter

Use generate method

result = client.generate(model='falcon-7b-instruct', data={
    "prompt": "Your prompt here",
    # ... other parameters
})

Quick Serve LLM

Launch a llama2-7b model using QuickServe API

Prepare and send payload to launch a LLM deployment. a. Choose Per_GPU_VRAM and GPU_Count based on your model size and batch size. Please see here for detailed list of supported model and infrastructure matrix.

launch_payload = {
    "basemodel_path": "meta-llama/Llama-2-7b-chat",
    "loramodel_path": "",
    "prompt_template": "{prompt}{completion}",
    "api_auth_token": "b6a97d3b-35d0-4720-a44c-59ee33dbc25b",
    "per_gpu_vram": 24,
    "gpu_count": 1
}

# Launch a deployment
ret = client.deploy("llm", launch_payload) 
deployment_id = ret.get("deployment_id")
print(ret)

# Get deployment status
status_ret = client.get_deployment_status(deployment_id)
print(status_ret)

logs_ret = client.get_deployment_logs(deployment_id)
print(logs_ret)

# Terminate Deployment
terminate_return = client.terminate_deployment(deployment_id)
print(terminate_return)

Run tests

Install test dependencies

pip install monsterapi[tests]

Run functional tests involving actual API key

export MONSTER_API_KEY=<your_api_key>
python3 -m pytest tests/ # Run all tests includes functional tests using actual API key

Run unit tests

export MONSTER_API_KEY="dummy"
python3 -m pytest tests/ -m "not slow" # Run only unit tests

PIP package push Instructions

pip install --upgrade setuptools wheel

python setup.py sdist bdist_wheel

pip install twine

twine upload dist/*

LLama Index CLient Usage

pip install llama_index llama-index-core llama-parse llama-index-readers-file 

from monsterapi.LLamaIndexClient import MonsterLLM

model = "meta-llama/Meta-Llama-3-8B-Instruct"
llm = MonsterLLM(
        model=model, temperature=0.1, max_tokens=256
    )

see examples/llama_index_and_chainlit/ for detailed chainlit example

About us

Check us out at monsterapi.ai

Checkout our new MonsterAPI Deploy service here

Check out new no-code finetuning service here

Checkout our Monster-SD Stable Diffusion v1.5 vs XL Comparison space here

Checkout our Monster API LLM comparison space here