Kumaran1987

  Source Code  Kaggle Notebook:  Space Research COT TOT Prompt - Gemma 2B Responses Google Colab:  space-research-cot-tot-prompt-gemma-2b-responses (1).ipynb - Colab   Leveraging Chain of Thoughts (CoT) and Tree of Thoughts (ToT) Models for Space Research and Innovation Subject Details: Introduction to CoT and ToT Methodologies : Overview of Chain of Thoughts (CoT) as a step-by-step reasoning framework. Introduction to Tree of Thoughts (ToT) for multi-branch explorations, allowing parallel evaluation of diverse hypotheses. Applications in Research Paper Development : Enhancing clarity and logical structuring in academic papers. Automating the drafting of hypotheses, problem-solving approaches, and iterative revisions. Innovation in Space Research : Hypothesis generation for challenges like propulsion systems or asteroid mining. Multi-path exploration to cover cost, feasibility, and ethical dimensions of research. Data-driven insights to predict space weather or ...

  As part of LLM KRL ( LLM Know Respect Language ) model this is the product feature choices fastest way to translate and comprehend !! Enabling Human Communication Multi Cultural and Multi Language support, still native people feel free to talk/ consult and communicate with respectful and fastest word exchanges !! Designing an LLM (Large Language Model) and GenAI (Generative AI) architecture for faster translations involves a combination of strategies to optimize model selection, training, inference, and deployment. Below is a step-by-step architecture design that includes the latest techniques for scalable and efficient translation. 1. High-Level Architecture Key Components: Data Preprocessing : Text cleaning, tokenization, and language alignment. Model Backbone : Efficient transformer-based models (e.g., MarianMT, BLOOM, or distilled versions of GPT). Training Optimization : Mixed precision, parameter-efficient fine-tuning (PEFT), and low-rank adaptation (LoRA). Inference ...

​​To develop a multi-level contextual classification model for English-to-Tamil sentiment analysis, incorporating Google's Gemma models can enhance performance, especially for Tamil language processing.​​ Here's a structured approach: 1. Data Preparation: Dataset Creation: ​​Compile a dataset containing English sentences, their Tamil translations, sentiment labels (positive/negative), and, for positive sentiments, an additional label indicating respect.​​ Example Data Structure: ​​| English Text                              | Tamil Translation                              | Sentiment | Respect (if Positive) | |-------------------------------------------|------------------------------------------------|-----------|-----------------------| | I am very happy to meet you               | உங்கள...