Present the core methodology, architecture, and empirical findings of our research paper clearly within a 10–15 minute conference talk. The presentation must articulate the specific research gap we addressed, explain how our LLM approach/framework works, and demonstrate why our results and benchmarks represent a meaningful advancement in the field.
Present the core methodology, architecture, and empirical findings of our research paper clearly within a 10–15 minute conference talk. The presentation must articulate the specific research gap we addressed, explain how our LLM approach/framework works, and demonstrate why our results and benchmarks represent a meaningful advancement in the field.
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Pragya-LM-160M is a lightweight decoder-only language model designed to make efficient NLP accessible under hardware and deployment constraints. Trained entirely from scratch on 5 billion tokens, it uses a custom tokenizer aligned with its corpus to build strong general-purpose representations. The project supports faster experimentation and practical language-model deployment in edge, academic, and resource-limited environments.