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GenAIOps with Prompt Flow is a "GenAIOps template and guidance" to help you build LLM-infused apps using Prompt Flow. It offers a range of features including Centralized Code Hosting, Lifecycle Management, Variant and Hyperparameter Experimentation, A/B Deployment, reporting for all runs and experiments and so on.
[Interspeech2025] Official implementation of Neuro2Semantic A Transfer Learning Framework for Semantic Reconstruction of Continuous Language from Human Intracranial EEG
This Streamlit app, "LangChain ChatBot," invites users to input queries, utilizing the LangChain library and OpenAI's text-davinci-003 model to generate responses with controlled randomness. In just a click, users can explore the intriguing world of conversation through this compact and user-friendly interface.
Ever thought of talking to your Email Inbox, like talking to a Real-human 😲. Well, you can do it completely on Device!! 🔥🔥🔥 No privacy issues. I used Chroma with Docker, Mistral-7B-Instruct, and Ollama.
"Dive into Generative AI and Large Language Models (LLMs) with our comprehensive learning roadmap! Explore the world of AI generation, from fundamental concepts to advanced techniques, through a structured journey designed to enhance your understanding and expertise."
Evaluation of Google's Instruction Tuned Gemma-2B, an open-source Large Language Model (LLM). Aimed at understanding the breadth of the model's knowledge, its reasoning capabilities, and adherence to ethical guardrails, this project presents a systematic assessment across a diverse array of domains.
new method for discovering vulnerabilities that employs a variety of methodologies. This method combines self-attention with convolutional networks to record both local, position-specific features and global, content-driven interactions
This study aims to fill this gap created by the exponential growth of digital content by evaluating the performance of selected LLMs in terms of summarization quality, using automated evaluation metrics. The goal is to identify the most effective models for generating accurate and human-like summaries, providing valuable insights for practitioners.
This project utilizes a machine learning model where consumer brand data is employed. Initially, a preliminary model is developed, followed by a refined model using a process called 'fine-tuning' to improve results. Additionally, a comprehensive testing suite has been created to validate accuracy and reliability of the model's predictions.
LLM-based variant extraction from title and abstracts of biomedical publications. Search literature-derived co-associations between variants, cancers, and treatments