PredictiveManish/Trimurti-LM

license: apache-2.0 tags:

• —multilingual
• —text-generation
• —indic-languages
• —hindi
• —punjabi
• —small-model pipeline_tag: text-generation widget:
• —text: "[EN] The weather today is" example_title: "English Generation"
• —text: "[HI] आज का मौसम" example_title: "Hindi Generation"
• —text: "[PA] ਅੱਜ ਦਾ ਮੌਸਮ" example_title: "Punjabi Generation" language:
• —en
• —hi
• —pa datasets:
• —ai4bharat/samanantar
• —PredictiveManish/multilingual-corpus library_name: transformers ---

Trimurti-LM: A 4.2M Parameter Multilingual Language Model

Model Description

Trimurti-LM is a small, efficient multilingual language model trained from scratch on English, Hindi, and Punjabi text. Named after the Hindu trinity (Brahma-Vishnu-Shiva), it represents the three-fold capability of creating text, preserving meaning, and transforming across scripts.

Key Features:

• —🏗️ Built from scratch - No pre-trained weights used
• —🌐 Multilingual - Handles 3 languages with 3 different scripts
• —💾 Tiny footprint - Only 4.2 million parameters
• —⚡ Fast training - 2.38 hours on consumer GPU (GTX 1650 4GB)
• —🔤 Smart tokenization - Custom SentencePiece with byte fallback for Indic scripts

Model Specifications

Training Data

The model was trained on a balanced multilingual corpus:

• —English: 150,000 sentences
• —Hindi: 150,000 sentences
• —Punjabi: 150,000 sentences

Sources:

• —Primary: AI4Bharat Samanantar dataset (filtered and processed)
• —Secondary: Custom curated multilingual corpus

Data Processing:

• —Language tagging: [EN], [HI], [PA] prefixes
• —Length filtering: 5-50 words per sentence
• —Script validation for each language
• —Deduplication and cleaning

Performance

Usage

Quick Start

from transformers import GPT2LMHeadModel
import sentencepiece as spm
import torch

# Load model and tokenizer
tokenizer = spm.SentencePieceProcessor()
tokenizer.load("multilingual_spm.model")
model = GPT2LMHeadModel.from_pretrained("PredictiveManish/Trimurti-LM")

# Generate text
prompt = "[EN] The weather is"
input_ids = tokenizer.encode(prompt)
input_tensor = torch.tensor([input_ids])

with torch.no_grad():
    output = model.generate(
        input_ids=input_tensor,
        max_length=50,
        temperature=0.7,
        do_sample=True,
        pad_token_id=0
    )

generated = tokenizer.decode(output[0].tolist())
print(generated)

citations(surely you're not going to use this but still, if in search of worst models):

If you use Trimurti-LM in your work, please cite:

@software{trimurti_lm_2026,
  title = {Trimurti-LM: A 4.2M Parameter Multilingual Language Model},
  author = {Manish Tiwari},
  year = {2026},
  url = {https://huggingface.co/PredictiveManish/Trimurti-LM},
  note = {Trained from scratch on English, Hindi, and Punjabi with consumer hardware}
}

Primary Dataset

@inproceedings{samanantar_2021,
  title = {Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages},
  author = {Gowtham Ramesh and Sumanth Doddapaneni and Aravinth Bheemaraj and Mayank Jobanputra and Raghavan AK and Ajitesh Sharma and Sujit Sahoo and Harshita Diddee and Mahalakshmi J and Divyanshu Kakwani and Navneet Kumar and Aswin Pradeep and Srihari Nagaraj and Kumar Deepak and Vivek Raghavan and Anoop Kunchukuttan and Pratyush Kumar and Mitesh Shantadevi Khapra},
  booktitle = {Proceedings of the Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks},
  year = {2021},
  url = {https://arxiv.org/abs/2104.05596}
}