ABIRGUJv1 is a custom transformer-based causal language model designed specifically for Gujarati and English text generation. It is trained from scratch using a custom tokenizer and architecture optimized for efficient Gujarati language understanding, translation, and generation.

This model focuses on providing lightweight, efficient Gujarati NLP capabilities while maintaining strong contextual understanding.

Model Details

Model Description

ABIRGUJv1 is a decoder-only transformer model trained entirely from scratch without using pretrained weights. It uses a custom tokenizer trained on Gujarati-focused data and is optimized for efficient inference and fine-tuning.

Developed by: Abir Maheshwari
Funded by: Independent Research
Shared by: Abir Maheshwari
Model type: Causal Language Model (Decoder-only Transformer)
Language(s): Gujarati, English
License: MIT
Finetuned from model: None (trained from scratch)

Model Sources

Repository: https://huggingface.co/abirmaheshwari/abirguJv1
Architecture: Custom Transformer
Framework: PyTorch + HuggingFace Transformers

Uses

Direct Use

ABIRGUJv1 is suitable for:

Gujarati text generation
English-to-Gujarati translation
Gujarati chatbots
Text completion
NLP research
Educational purposes

Example applications:

Gujarati AI assistants
Content generation
Research on small language models

Downstream Use

This model can be fine-tuned for:

Gujarati instruction models
Question answering
Domain-specific NLP tasks
Gujarati conversational agents

Out-of-Scope Use

Not recommended for:

Critical decision systems
Medical diagnosis
Legal advice
Safety-critical applications This is an early version model.

Bias, Risks, and Limitations

ABIRGUJv1 may:

Produce incorrect information
Reflect biases present in training data
Generate incomplete or nonsensical outputs This is expected for models trained on limited datasets.

Recommendations

Use this model:

For research
For experimentation
For fine-tuning Not recommended for production without further training.

How to Get Started

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abirmaheshwari/abirguJv1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("abirmaheshwari/abirguJv1", trust_remote_code=True)

input_text = "મારું નામ"

inputs = tokenizer(input_text, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_length=100,
    temperature=0.7
)
print(tokenizer.decode(outputs[0]))

abirmaheshwari/abirgujv1

ABIRGUJv1 — Gujarati-First Transformer Language Model