replit

Replit Code V1.5 3B

Language: Python

API: MAX Graph

This pipeline demonstrates code completion from an initial prompt using Replit's Code V1.5 3B large language model. The model itself has been constructed in Python using the MAX Graph API.

The MAX Graph API provides an accessible interface to the construction of flexible accelerated compute graphs, which are then optimized by the MAX Engine's advanced graph compiler. This pipeline showcases how a large language model can be fully defined using Python and MAX Graphs and then compiled for optimal inference performance via the MAX Engine.

Model

Replit Code is an open source code generation model trained on permissively licensed code and released by Replit. The V1.5, 3B variant is the basis for this implementation, and weights are obtained via Hugging Face.

Usage

The easiest way to try out this pipeline is with our Magic command-line tool.

1. Install Magic on macOS and Ubuntu with this command:

   curl -ssL https://magic.modular.com | bash

   Then run the source command that's printed in your terminal.

   To see the available commands, you can run magic --help. Learn more about Magic here.

2. Clone the MAX examples repository:

   If you don't already have a local clone of this repository, create one via:

   git clone https://github.com/modularml/max.git

   The following instructions assume that you're present within this pipeline's directory, and you can change to it after cloning:

   cd max/pipelines/python/

3. Now run the Replit code completion demo with the following command:

   magic run replit --prompt "def fibonacci(n):"

4. Host a code completion endpoint via MAX Serve.

   MAX Serve provides functionality to host performant OpenAI compatible endpoints using the FastAPI framework.

   You can configure the pipeline to be hosted by using the --serve argument. For example:

   magic run replit --serve

   A request can be submitted via a cURL command.

   curl -N http://localhost:8000/v1/chat/completions \
   -H "Content-Type: application/json" \
   -d '{
       "model": "replit/replit-code-v1_5-3b",
       "stream": true,
       "messages": [
           {"role": "system", "content": "You are a helpful assistant."},
           {"role": "user", "content": "def fibonacci(n)"}
       ]
   }'

Options

The following command-line options are available to customize operation of the pipeline:

• --max-length: Controls the maximum length of the text sequence (includes the input tokens). (Default value: 512)
• --max-new-tokens: The maximum number of new tokens to generate. If a -1 value is provided, the model will continue to generate tokens for the entire context length. (Default value: -1)
• --prompt: The text prompt to use for further generation.
• --quantization-encoding: The encoding to use for a datatype that can be quantized to a low bits per weight format. The options for quantized formats will download and cache default weights, but float32 requires the use of --weight-path to specify locally downloaded full-precision weights for use in the model. Valid values: bfloat16, float32. (Default value: float32).
• --save-to-serialized-model-path: If specified, writes the serialized model to this path.
• --serialized-model-path: If specified, tries to load a serialized model from this path.
• --top-k: Limits the sampling to the K most probable tokens. Default is 1.
• --weight-path: Overrides the default URL, and allows for an already-downloaded pretrained weight file to be used with the model.
• --max-cache-batch-size: Specifies the maximum batch size to be used. Default is 1.
• --use-gpu: Uses the GPU to execute the model.