metadata

base_model:
  - google/gemma-2-2b-it
library_name: transformers
license: apache-2.0
language:
  - sw
  - en
metrics:
  - accuracy
  - bleu
pipeline_tag: text2text-generation
datasets:
  - fka/awesome-chatgpt-prompts
tags:
  - finance
  - AI
  - NLP
  - customer-support
new_version: google/gemma-2-27b-it

Model Card for Futuresony AI

Model Details

Model Description

Futuresony AI is a fine-tuned language model designed to enhance conversational AI in customer support, finance, and general-purpose NLP applications. It is developed with a focus on multilingual capabilities, supporting both Swahili and English. The model is particularly fine-tuned for tasks such as text generation, question-answering, and AI-driven customer support solutions, aligning with Salum A. Salum’s goal of introducing AI-driven automation in Tanzania.

Developed by: Futuresony Tech
Funded by: Futuresony
Shared by: Salum A. Salum
Model type: Transformer-based text-to-text generation model
Language(s) (NLP): Swahili and English
License: Apache 2.0
Finetuned from model: google/gemma-2-2b-it

Model Sources

Repository: [More Information Needed]
Paper: [More Information Needed]
Demo: [More Information Needed]

Uses

Direct Use

The model can be used for:

AI-powered customer support for businesses
Financial query handling
General NLP tasks like summarization, translation, and chatbot applications
Integration with WhatsApp, SMS, and social media for automated responses

Downstream Use

When fine-tuned, the model can be applied to:

ASA Microfinance, model finetuned with ASA Microfinance Company to assist internal and external users related to ASA questions
AI-driven traffic control and safety enhancements at Tanga Port
AI-driven FAQ systems for businesses

Out-of-Scope Use

Any application that violates ethical AI usage, such as misinformation, hate speech, or unethical automation.
Legal or medical advisory applications without proper expert supervision.

Bias, Risks, and Limitations

While Futuresony AI is trained to provide accurate and helpful responses, it may still exhibit biases present in the training data. There is also a risk of incorrect or misleading responses, particularly for sensitive topics.

Recommendations

Users should verify important responses with human oversight, especially in critical domains like finance and customer support. Continuous monitoring and retraining are recommended to improve accuracy and fairness.

How to Get Started with the Model

To use the model, follow this example:

from transformers import pipeline

generator = pipeline("text2text-generation", model="Futuresony/future_ai_12_10_2024.gguf")
response = generator("What is the best way to improve AI chatbot accuracy?")
print(response)

Training Details

Training Data

The model is fine-tuned using a dataset that includes:

Customer service inquiries and responses
Financial support queries
Swahili-English multilingual conversations

Training Procedure

Preprocessing: Tokenization and normalization of data
Training Regime: Mixed precision (fp16) for efficiency
Hardware: Trained using Google Colab with TPU acceleration

Evaluation

Testing Data, Factors & Metrics

Testing Data

Datasets used: fka/awesome-chatgpt-prompts
Additional customer service data from Futuresony’s internal datasets

Factors

Response accuracy
Coherence in multilingual interactions
Efficiency in real-time conversations

Metrics

Accuracy: Measures the correctness of generated responses
BLEU Score: Evaluates text coherence and fluency

Results

Initial tests show strong performance in customer service scenarios, with high coherence and accuracy in answering user queries in both Swahili and English.

Model Examination

Further research is needed to evaluate:

Bias mitigation
Performance in domain-specific use cases

Environmental Impact

Training energy consumption:

Hardware Type: Google Colab TPU
Hours used: ~50 hours
Cloud Provider: Google Cloud
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications

Model Architecture and Objective

Futuresony AI is based on the Google Gemma-2 architecture and fine-tuned for NLP tasks with a focus on customer support and finance applications.

Compute Infrastructure

Hardware: TPU-based training
Software: Hugging Face Transformers, PyTorch

Citation

If you use Futuresony AI in your research or project, please cite:

@misc{FuturesonyAI2025,
  author = {Salum A. Salum},
  title = {Futuresony AI: A Multilingual Conversational Model},
  year = {2025},
  url = {https://huggingface.co/Futuresony}
}

Model Card Contact

For inquiries or collaboration, contact:

Email: [email protected]
Phone: +255 672 087 616

This model card provides an overview of Futuresony AI, detailing its purpose, training, and usage guidelines. The information is subject to updates as the project evolves.