MPT-7B Base Truss

This is a Truss for MPT-7B using the mosaicml/mpt-7b family of models from the transformers library. This README will walk you through how to deploy the base version on Baseten.

Truss

Truss is an open-source model serving framework developed by Baseten. It allows you to develop and deploy machine learning models onto Baseten (and other platforms like AWS or GCP. Using Truss, you can develop a GPU model using live-reload, package models and their associated code, create Docker containers and deploy on Baseten.

Deploying MPT-7B

To deploy the MPT-7B Base Truss, you'll need to follow these steps:

1. Prerequisites: Make sure you have a Baseten account and API key. You can sign up for a Baseten account here.

2. Install Truss and the Baseten Python client: If you haven't already, install the Baseten Python client and Truss in your development environment using:

pip install --upgrade baseten truss

3. Load the MPT-7B Base Truss: Assuming you've cloned this repo, spin up an IPython shell and load the Truss into memory:

import truss
mpt_truss = truss.load("path/to/mpt_truss")

4. Log in to Baseten: Log in to your Baseten account using your API key (key found here):

import baseten

baseten.login("PASTE_API_KEY_HERE")

5. Deploy the MPT-7B Base Truss: Deploy MPT-7B Base to Baseten with the following command:

baseten.deploy(mpt_truss)

Once your Truss is deployed, you can start using MPT-7B Base through the Baseten platform! Navigate to the Baseten UI to watch the model build and deploy and invoke it via the REST API.

MPT-7B API documentation

This section provides an overview of the MPT-7B Base API, its parameters, and how to use it. The API consists of a single route named predict, which you can invoke to generate text completions based on the provided parameters.

API route: predict

The predict route is the primary method for generating images based on a given set of parameters. It takes several parameters:

• prompt: The input text prompt for the LLM
• max_tokens: (optional) The maximum number of tokens that will be generated
• temperature: (optional) A temperature of 0 means the response is more deterministic. A temperature of greater than zero results in increasing variation in the completion.
• top_k: (optional) Controls how model picks the next token from the top k tokens in its list, sorted by probability.
• top_p: (optional) Controls how the model picks from the top tokens based on the sum of their probabilities.

Example usage

You can use the baseten model package to invoke your model from Python

import baseten
# You can retrieve your deployed model ID from the UI
model = baseten.deployed_model_version_id('YOUR_MODEL_ID')

request = {
    "prompt" : "Today I inspected the engine mounting equipment. I found a problem in one of the brackets so"
    "temperature": 0.75,
    "max_tokens": 200
}

response = model.predict(request)

You can also invoke your model via a REST API

curl -X POST " https://app.baseten.co/models/YOUR_MODEL_ID/predict" \
     -H "Content-Type: application/json" \
     -H 'Authorization: Api-Key {YOUR_API_KEY}' \
     -d '{
    "prompt" : "Today I inspected the engine mounting equipment. I found a problem in one of the brackets so" \
    "temperature": 0.75, \
    "max_tokens": 200 \
}'