Stable Diffusion Task

A general framework to define and execute the llm text generation task.

Features

• Unified task definition for various different large language model
• Apply model specific chat templates to input prompts automatically
• Model quantizing (INT4 or INT8)
• Fine grained control text generation arguments
• ChatGPT style response

Example

Here is an example of the gpt2 text generation:

import logging
from gpt_task.inference import run_task


logging.basicConfig(
    format="[{asctime}] [{levelname:<8}] {name}: {message}",
    datefmt="%Y-%m-%d %H:%M:%S",
    style="{",
    level=logging.INFO,
)

messages = [{"role": "user", "content": "I want to create a chat bot. Any suggestions?"}]


res = run_task(
    model="gpt2",
    messages=messages,
    seed=42,
)
print(res)

Get started

Create and activate the virtual environment:

$ python -m venv ./venv
$ source ./venv/bin/activate

Install the dependencies and the library:

(venv) $ pip install -r requirments.txt && pip install -e .

Check and run the examples:

(venv) $ python ./examples/gpt2_example.py

More explanations can be found in the doc:

https://docs.crynux.ai/application-development/gpt-task

Task Definition

The complete task definition is GPTTaskArgs in the file ./src/gpt_task/models/args.py

Task Response

The task response definition is GPTTaskResponse in the file ./src/gpt_task/models/args.py

JSON Schema

The JSON schemas for the tasks could be used to validate the task arguments by other projects. The schemas are given under ./schema. Projects could use the URL to load the JSON schema files directly.