textjenerator 0.1.0

AI text generator

Text Jenerator

A Unified Python Interface for Modular Text Generation.

Text Jenerator is a framework designed to provide a consistent, abstract interface for deploying and interacting with various Large Language Models (LLMs). By separating the generic configuration and workflow from the model-specific implementation, it enables you to easily switch between different model backends—such as local GGUF models (via Llama-CPP), Hugging Face Transformers, or proprietary APIs (e.g., OpenAI, Gemini), with no code changes besides the config you pass.

Features

• Modular Architecture: Uses Abstract Base Classes (ABC) to enforce a clean separation between the model details, meaning you only need to instantiate the TextGenerator class and call generate_text(), no matter which model you're using.
• Automatic Setup: Handles generic tasks like device detection (CPU/CUDA) and data type selection (bfloat16, float16, float32) in the base class.
• Plug-and-Play Models: Easily integrate new models by creating concrete child classes that implement two simple abstract methods (create_pipeline and run_pipeline).
• Model Registry: Uses a decorator (@register_model) to automatically track and load available generation backends.

Installation

pip install textjenerator

The core package has minimal dependencies. Model-specific implementations (e.g., for Llama-CPP or Transformers) require their respective libraries.

Models

Text Jenerator is BYOM. You can put the local model path or a HuggingFace ID in your config.

Quick Start & Usage

Config

Your project should include a config file (defaults are at textjenerator.config). A basic config might look like this:

config = {
    # model
    "model": "llama-cpp",
    "model_path": ".models/Llama-3.2-3B-Instruct-Q4_K_M.gguf",

    # hardware/system
    "device": "cpu",
    "dtype": "float32",

    # LLM
    "max_context_size": 4096,
    "number_of_threads": 8 ,
    "verbose_warnings": False,
    "messages_to_keep_in_context": 4,
    "max_tokens_per_response": 256,
    "temperature": 0.7 ,
    "top_p": 0.9,
    "top_k": 50,
    "messages": [ 
          {"role": "system", "content": """You are Jenbot, an expert, helpful, and diligent assistant. You provide the user with accurate answers to their queries. You are polite, friendly, and a little sarcastic."""},
          {"role": "user", "content": """Hi, who are you?"""},
    ]
}

Run the Generation

To run the main workflow:

from textjenerator.models import registry
from config import config # or load your config however you prefer

text_generator = registry.get_model_class(config) # Choose which model you want from the registry
response = text_generator.generate_text() # create pipeline and run it
print(response)

You can also do these steps separately if you want to keep the model in memory. In this case you can pass a new config, typically this will be to include the cumulative chat history, but you could also change other settings on the fly e.g.:

• Reduce temperature for a coding question
• Increase temperature for a creative writing request
• Increase max_tokens_per_response if you expect a longer reply

text_generator = registry.get_model_class(config)
text_generator.create_pipeline()
response = text_generator.run_pipeline()
print(response)

# or pass another, doesn't have to be the whole thing:
new_config = {
    "messages": [ 
        {"role": "system", "content": """You are Jenbot, an expert, helpful, and diligent assistant. You provide the user with accurate answers to their queries. You are polite, friendly, and a little sarcastic."""},
        {"role": "user", "content": """Hi, who are you?"""},
        {"role": "assistant", "content": """You must be wondering who I am. Well, let me introduce myself: I'm Jenbot, your friendly AI assistant. I'm here to help answer any questions you may have, provide information on a wide range of topics, and even offer a dash of sarcasm when the situation calls for it."""},
        {"role": "user", "content": """What are your thoughts on the hard problem of consciousness?"""},
    ]
}
response = text_generator.run_pipeline(new_config)
print(response)