File size: 2,740 Bytes
3c6543d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
---
license: apache-2.0
datasets:
- legacy-datasets/common_voice
language:
- en
- sw
metrics:
- perplexity
base_model:
- mistralai/Mistral-Small-24B-Instruct-2501
pipeline_tag: text-generation
tags:
- community
- surveys
- engagement
- referral-tracking
- community-engagement
---
# Stahili LLM
[](LICENSE)
## 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:
```bash
pip install transformers torch
```
Alternatively, load it via the Hugging Face model hub:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "itshunja/stahili"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
```
## Usage
### Generating Text
```python
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:
```bash
python train.py --model itshunja/stahili --dataset custom_dataset.json
```
## API Integration
Use the Hugging Face Inference API:
```python
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](LICENSE) file for details.
## Contact
For questions or support, reach out via [Hugging Face Discussions](https://huggingface.co/spaces) or contact Isaac Hunja. |