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Awesome Reasoning LLM Tutorial/Survey/Guide

Train Large Language Models on MLX.

Tools made for usage alongside artistic style transfer projects

RAFT, or Retrieval-Augmented Fine-Tuning, is a method comprising of a fine-tuning and a RAG-based retrieval phase. It is particularly suited for the creation of agents that realistically emulate a specific human target.

Developed a sophisticated machine learning model capable of generating diverse interview questions aligned with specific topics, ensuring depth of conversation. Integrated advanced Natural Language Processing (NLP) algorithms to analyse spoken responses, identifying grammatical errors & offering accurate corrections after the interview.

Group-relative Trajectory-based Policy Optimization: Increasing Quality and Training Stability

🧹 Gracefully shutdown Node.js application: help you handle exit signals and cleanup

🍎👉🍏 Everything you need in order to get started building distributed machine learning with Apple's MLX

Natural Language Processing (NLP) and Large Language Models (LLM) with Fine-Tuning LLM and make Chatbot Question answering (QA) with LoRA and Flan-T5 Large

DantinoX: A modular, memory-efficient Transformer implementation in JAX/Flax NNX. Includes Sparse MoE, GQA, Sliding Window Attention, Gradient Accumulation and Checkpointing

Tutorials for the U Toronto Foundation Models for Science Workshop

Fyne Thyngges provided by Learning Objects, for Use with ScalaZ and Libraries of a similar Nature and Quality.

A fine-tuned version of Phi-3-mini-4k-instruct for generating SQL queries from natural language prompts, utilizing synthetic datasets and QLoRA for efficient adaptation and deployment.

Proyek Ollama DeepSeek Llama RAG (Retrieval-Augmented Generation) - Fine-Tuned Proyek ini bertujuan untuk membuat sebuah Generative AI yang dapat membuat sebuah module ajar yang dapat digunakan oleh guru untuk mengajar siswa.

For conceptual understanding you can refer my medium blog which will provide you in-depth knowledge of SVC along with various other factors required in data science.

Cross-platform Flutter AI chat application for Android, Linux, and Windows. Features a fine-tuned agent capable of training other agents (like instructors) using Google's new A2A service.

LLM Fine-tuning Challenge Enhancing Qwen 2.5 3B for AI Research QA

AI-powered bank statement analyzer for secure transaction extraction, intelligent categorization, anomaly detection, and financial insight generation.

Fine grained sentiment analysis on App Reviews with deep learning.

End-to-end QLoRA fine-tuning pipeline for LLM function calling, covering structured dataset preparation, parameter-efficient training, systematic evaluation, inference, and model management.