Model Card for AI Agni L4 Base

Model Details

Model Description

AI Agni L4 Base is an Indian-based foundational language model developed by Endurasolution, Kerala, India. It is designed to support multilingual tasks with a special emphasis on Indian regional languages such as Malayalam, Hindi, Tamil, and others.
This model aims to serve as a baseline for developing further domain- and task-specific applications. Due to its experimental nature and the diversity of its training data—which spans public datasets and regional Indian resources—users might encounter various errors or issues that will be addressed in future iterations.

Developed by: Endurasolution, Kerala, India
Funded by: Indian research initiatives and private investments by Endurasolution
Shared by: Endurasolution Team
Model type: Pretrained Transformer-based Language Model (AI Agni L4 Base)
Language(s): English, Malayalam, Hindi, Tamil, and other regional languages
License: Apache-2.0
Finetuned from model: GPT-2 architecture (with modifications for multilingual and regional language support)

Model Sources

Repository: [TBD: Provide GitHub/Hub link for AI Agni L4 Base]
Paper: [TBD: Link to technical report or white paper]
Demo: [TBD: Link to a live demo if available]

Uses

Direct Use

AI Agni L4 Base is intended for tasks such as language understanding, text generation, and conversational AI tailored for Indian users. It supports queries and responses in English as well as major Indian languages.

Downstream Use

This foundational model can be further fine-tuned for specific downstream tasks like machine translation, sentiment analysis, summarization, and more, especially within contexts requiring support for regional languages and culturally specific nuances.

Out-of-Scope Use

The model is experimental and might yield errors or inadequate responses when applied to safety-critical tasks.
It is not recommended for legal, medical, or high-stakes decision-making applications without further fine-tuning and validation.
The model is not designed to support malicious applications such as misinformation or hate speech propagation.

Bias, Risks, and Limitations

Despite efforts to include diverse data sources and regional languages, AI Agni L4 Base may reflect biases present in its training data. Users should be aware that:

Cultural and linguistic nuances of all Indian languages may not be perfectly captured.
As a baseline model, it is subject to errors, inaccuracies, and might require further fine-tuning for robust performance.
Privacy-preserving measures have been integrated during data curation; however, users must ensure that any deployment complies with local privacy laws and ethical guidelines.

Recommendations

Evaluation: Thoroughly evaluate the model on your specific datasets before deployment.
Fine-Tuning: Consider additional fine-tuning using domain-specific and culturally relevant data.
Monitoring: Implement robust monitoring strategies to mitigate any unintended biases or errors.

How to Get Started with the Model

Below is an example code snippet to load and use AI Agni L4 Base with Hugging Face's Transformers library:

from transformers import GPT2Tokenizer, GPT2LMHeadModel

model_name = "path/to/ai-agni-l4-base"  # Update with the actual model path or identifier
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

input_text = "Question: What is AI Agni L4 Base?\nAnswer:"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))