gstop 0.2.4

Generation Stopping Criteria for transformers Language Model

gstop

gstop is a Python library that provides generation stopping criteria for Transformers-based language models. It allows you to define custom stop tokens and criteria to control the generation process and prevent the model from generating unwanted or irrelevant content.

Features

• Define custom stop tokens and criteria for language model generation
• Supports various pre-defined stop token registries for popular language models
• Easy integration with the Transformers library
• Flexible and extensible architecture for adding new stop token registries

Installation

You can install gstop using pip:

pip install gstop

Usage

Here's a basic example of how to use gstop with the Transformers library:

from gstop import GenerationStopper, STOP_TOKENS_REGISTRY
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "mistralai/Mistral-7B-v0.1"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
stopper = GenerationStopper(STOP_TOKENS_REGISTRY["mistral"])

input_ids = tokenizer("Hello, world!", return_tensors="pt").input_ids

out = model.generate(input_ids, stopping_criteria=stopper.criteria)
print(stopper.format(tokenizer.decode(out[0])))

In this example, we create an instance of GenerationStopper using the pre-defined stop tokens registry for the "mistral" model. We then use the generate method of the language model to generate text, passing the stopping_criteria parameter with the stopper's criteria. Finally, we format the generated text using the format method of the stopper to remove any stop tokens.

Customization

You can customize the stop tokens and criteria by creating your own stop token registry or by modifying the existing ones. The stop token registries are defined in the common.py file.

To create a new stop token registry, you can add an entry to the STOP_TOKENS_REGISTRY dictionary with the desired stop tokens and their corresponding token IDs.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on the GitHub repository.