Stahili LLM

Overview

Stahili LLM is a large language model designed for community-driven insights, localized interactions, and engagement tracking. Built with a focus on user participation, it facilitates structured data collection, analytics, and automation in survey-based applications.

Features

• Conversational AI: Trained to understand and generate human-like text.
• Survey and Referral Optimization: Helps track user participation and referrals.
• Customizable Workflows: Supports integration into diverse applications.
• Multilingual Support: Can process multiple languages, enhancing accessibility.
• Open-Source & Extensible: Licensed under Apache 2.0, allowing modifications and contributions.

Installation

To use Stahili LLM, you can either install it via pip or run it using Hugging Face's API:

pip install transformers torch

Alternatively, load it via the Hugging Face model hub:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "itshunja/stahili"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

Usage

Generating Text

input_text = "How does Stahili optimize survey engagement?"
inputs = tokenizer(input_text, return_tensors="pt")
output = model.generate(**inputs, max_length=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Fine-Tuning

To fine-tune Stahili LLM on a specific dataset:

python train.py --model itshunja/stahili --dataset custom_dataset.json

API Integration

Use the Hugging Face Inference API:

from transformers import pipeline

generator = pipeline("text-generation", model="itshunja/stahili")
response = generator("Explain the Stahili rewards program.")
print(response[0]['generated_text'])

Contributing

We welcome contributions! To contribute:

1. Fork this repository.
2. Create a feature branch (git checkout -b feature-name).
3. Commit changes (git commit -m 'Add new feature').
4. Push to your branch (git push origin feature-name).
5. Submit a Pull Request.

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.

Contact

For questions or support, reach out via Hugging Face Discussions or contact Isaac Hunja.