md-nishat-008/TigerLLM-1B-it
TigerLLM-1B-it is a 1 billion parameter instruction-tuned causal language model developed by Nishat Raihan and Marcos Zampieri from George Mason University. Part of the TigerLLM family, this model is specifically designed and optimized for the Bangla language, leveraging a 10M-token educational corpus and a 100K native instruction dataset. It aims to address the linguistic disparity in LLM development for low-resource languages, establishing a new baseline for Bangla language modeling.
Loading preview...
TigerLLM-1B-it: A Specialized Bangla Language Model
TigerLLM-1B-it is a 1 billion parameter instruction-tuned model from the TigerLLM family, developed by Nishat Raihan and Marcos Zampieri at George Mason University. This model is specifically engineered to address the significant gap in high-quality, reproducible Large Language Models for Bangla, the world's 5th most spoken language. It is built upon a LLaMA-3.2 base and has been continually pre-trained and fine-tuned using unique, high-quality Bangla datasets.
Key Capabilities & Differentiators
- Bangla-Centric Design: Developed from the ground up for the Bangla language, utilizing a 10M-token "Bangla-TextBook" corpus derived from authentic educational materials (Grades 6-12).
- High-Quality Instruction Following: Fine-tuned on "Bangla-Instruct," a 100K instruction-response dataset generated via a self-instruct framework with GPT-4 and Claude-3.5-Sonnet, ensuring native linguistic quality and cultural sensitivity.
- Superior Performance: Benchmarked against multiple Bangla-specific evaluations (MMLU-bn, PangBench-bn, BanglaQuaD, mHumanEval-bn, BEnQA, BanglaRQA), TigerLLM-1B-it demonstrates performance that surpasses existing open-source Bangla LLMs and even larger proprietary models like GPT-3.5 in several metrics.
- Reproducibility: Emphasizes transparent methodology and dataset creation to ensure reproducibility, a common limitation in previous Bangla LLM initiatives.
Good For
- Bangla NLP Applications: Ideal for developers and researchers building applications requiring robust understanding and generation in Bangla.
- Educational Tools: Particularly well-suited for tasks related to educational content, given its training on the Bangla-TextBook corpus.
- Research in Low-Resource Languages: Serves as a strong baseline for further research and development in Bangla and other low-resource language LLMs.