Text Generation
Transformers
Safetensors
English
Hindi
Panjabi
language_model
multilingual
indic-languages
hindi
punjabi
small-model
Instructions to use PredictiveManish/Trimurti-LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PredictiveManish/Trimurti-LM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PredictiveManish/Trimurti-LM")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PredictiveManish/Trimurti-LM", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use PredictiveManish/Trimurti-LM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PredictiveManish/Trimurti-LM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PredictiveManish/Trimurti-LM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/PredictiveManish/Trimurti-LM
- SGLang
How to use PredictiveManish/Trimurti-LM with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "PredictiveManish/Trimurti-LM" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PredictiveManish/Trimurti-LM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "PredictiveManish/Trimurti-LM" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PredictiveManish/Trimurti-LM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use PredictiveManish/Trimurti-LM with Docker Model Runner:
docker model run hf.co/PredictiveManish/Trimurti-LM
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citations(surely you're not going to use this but still, if in search of worst models):
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title = {Trimurti-LM: A 4.2M Parameter Multilingual Language Model},
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author = {Manish},
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year = {
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url = {https://huggingface.co/PredictiveManish/Trimurti-LM},
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note = {Trained from scratch on English, Hindi, and Punjabi with consumer hardware}
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}
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---
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```
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## citations(surely you're not going to use this but still, if in search of worst models):
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If you use Trimurti-LM in your work, please cite:
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```bibtex
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@software{trimurti_lm_2026,
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title = {Trimurti-LM: A 4.2M Parameter Multilingual Language Model},
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author = {Manish Tiwari},
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year = {2026},
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url = {https://huggingface.co/PredictiveManish/Trimurti-LM},
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note = {Trained from scratch on English, Hindi, and Punjabi with consumer hardware}
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}
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### Primary Dataset
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```bibtex
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@inproceedings{samanantar_2021,
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title = {Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages},
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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},
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booktitle = {Proceedings of the Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks},
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year = {2021},
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url = {https://arxiv.org/abs/2104.05596}
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}
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---
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