Projects

Overview: Grok Imagine 2.0 API is a powerful tool for image generation and region-editing, provided through the OpenAI-compatible APIMart endpoint. It offers a flexible catalog of models and pricing options, making it accessible and cost-effective. Key features: - Access to Grok Imagine 2.0 (model id: `grok-imagine-2.0-ext`) via a single base URL and `Authorization` header. - No need for monthly subscriptions or pre-paid plans; charges are based on usage. - Returns the exact amount charged for completed tasks. - Designed for non-blocking requests, polling the `GET /v1/tasks/{id}` endpoint is sufficient. Tech stack: Python Why it matters: This API simplifies the process of accessing Grok Imagine 2.0 models, providing a straightforward and cost-effective solution for developers looking to integrate image generation into their projects.

### Overview: Grok Imagine 2.0 Prompts: 8 Tested Recipes with Outputs ### Key Features: - **Model:** `grok-imagine-2.0-ext` - **Recipes:** 8 (machine-readable in `data/prompts.json`, table in `PROMPTS.md`) - **Effective Price:** $0.015 per image ### Tech Stack: - Python ### Why It Matters: - **Key Features:** The prompts are tested and provide clear, effective image generation recipes. The model can be accessed via a single API key, making it easy to integrate into projects without the need for expensive subscription plans. The pricing is straightforward, with charges only applied to the actual images generated, making it cost-effective for small-scale projects. ### Repository: - **GitHub Repository:** yx0gie/grok-imagine-2-0-prompts-ja - **Stars:** 7 - **Description:** Grok Imagine 2.0 prompts: tested image prompt recipes with example renders, aspect ratios, the model id (`grok-imagine-2.0-ext`), and the cost per image. The API endpoint is tested and the pricing is explained. ### Team Use Case: - **Key Features:** The prompts are designed to be easily integrated into projects without the need for expensive subscription plans. The pricing is straightforward, with charges only applied to the actual images generated, making it cost-effective for small-scale projects. The model can be accessed via a single API key, making it easy to integrate into projects without the need for expensive

Overview: TurboQuantPlayground is a project that ported Google's TurboQuant from NVIDIA Triton to Apple Silicon, focusing on KV cache compression and leveraging MLX Metal kernels. This fork includes a plain CMake C++17 implementation with a header-only public API, making it automotive-portable and zero OS dependencies in the core. Key features: - A plain CMake C++17 implementation with a header-only public API - 4 fully-implemented compute backends: CPU scalar, ARM NEON, Adreno OpenCL, Adreno Vulkan, and QNN/HTP scaffold - 4 fully-implemented target platforms: x86 + NVIDIA, Apple Silicon, Snapdragon mobile (S24 Ultra / Tab S9+) + automotive-ready toolchain stubs (QNX aarch64, Linux aarch64 for SA8155P / SA8295P / SA8775P) - Vendored fork of llama.cpp with a new `llama_kv_cache_turboquant` class - VLM pipeline with a persistent in-process mtmd JNI session - On-device app with 4 tabs (Assistant, Live, Bench, Settings) and 12 Android-Intent tools - Camera VLM running in on-device captioning with a single-flight gate and works in airplane mode Tech stack: Plain CMake C++17 with a header-only public API Why it matters: TurboQuant cuts on-device LLM memory by 4x with no quality loss, lights up CPU + GPU

Overview: This platform is designed for users to easily fine-tune and test their models, providing a user-friendly environment for handling file management and interacting with the trained models via a web interface. Key features: - Fine-tuning capabilities - File API integration: List, upload, delete, retrieve, and fetch file information - Fine-tuning API: Create, retrieve, cancel, list events, obtain result models, and test models - WebUI: Sleek interface for interacting with all features Tech stack: JavaScript Why it matters: This platform simplifies the process of fine-tuning and testing models, making it accessible and efficient for users without requiring extensive technical knowledge.

Overview: This template is designed for training multiple concepts and regularization images in Low-rank Adaptation diffusion models. Key features: - Multiple concept loRA structure - Balanced datasets for concept repetitions - Configurable training sessions Tech stack: Python Why it matters: This template streamlines the training process for Lora models, ensuring efficient and effective concept and regularization image training.

### Overview: Autosynth is an agentic synthetic-data generation framework inspired by Meta FAIR's Autodata/Agentic Self-Instruct paper. It generates structured examples using an LLM loop that proposes, checks, solves, and scores its own examples. The framework is designed to be task-specific, allowing it to generate math problems, support-ticket data, document-grounded QA, and more. ### Key features: - **Task-Specific Behavior**: The same runtime can generate examples for various tasks, including math problems, support-ticket data, and document-grounded QA. - **Quality Checks**: Candidates are evaluated for obvious problems, and weak and strong solvers attempt the input multiple times. - **Feedback Loop**: Failed attempts become feedback for the next round, improving the quality of the generated examples. - **Acceptance Modes**: Acceptance can be set to rubric, verifiable, or judge, each with its own scoring criteria. ### Tech stack: Python ### Why it matters: Autosynth is an innovative tool for generating synthetic data, inspired by Meta FAIR's cutting-edge approach. It addresses the need for high-quality, structured examples that can be used for training and testing machine learning models. The framework's ability to generate diverse and relevant examples makes it a valuable asset in the development and validation of AI systems.

Overview: AuraLite AI is a tool designed to train and fine-tune large language models on a personal computer without the need for a GPU. It supports various models like Qwen3.5, Qwen2.5/3, Llama, Mistral, and SmolLM2, and allows users to fine-tune these models using QLoRA and train small models from scratch. Key features: - Fine-tuning Qwen3.5 (2026) and Qwen2.5/3 with QLoRA on a CPU - Training small models from scratch - Exporting models to formats compatible with Ollama, LM Studio, and transformers - Running models locally using PyTorch - Fine-tuning and training from scratch with a wizard interface - Providing a Windows app and command line interface - Supporting multiple languages (English and Russian) Tech stack: AuraLite AI uses PyTorch for training and runs on a computer without a GPU. Why it matters: AuraLite AI simplifies the process of training and fine-tuning large language models, making it accessible to users without the resources needed for GPU-based training. This tool is particularly useful for developers and enthusiasts who want to experiment with cutting-edge language models without the need for specialized hardware.

Overview: Fine-tuning Google's BERT model on text classification for sentiment analysis. Key features: - Fine-tuning BERT model on text classification tasks - Utilizing PyTorch and Keras for implementation - Transfer learning approach for sentiment analysis Tech stack: PyTorch, Keras Why it matters: This project demonstrates how to apply BERT for sentiment analysis, showcasing the effectiveness of transfer learning in text classification tasks.

Overview: This utility uses face recognition to automatically crop faces out of multiple images, making it easier to create datasets with faces cropped properly. Key features: - Automatically crops faces out of multiple images - Works even when there are multiple people in the pictures - Uses the face-recognition package for recognition Tech stack: Python Why it matters: This utility streamlines the process of cropping faces out of images, which can be time-consuming and error-prone when done manually.

Overview: This repository features a collection of generative AI applications designed to showcase the capabilities of AI technology across various domains. Key features: - Text summarization projects - Text generation projects - Text classification projects - Image synthesis models - Code generation tools Tech stack: Python, TensorFlow, PyTorch, Groq API, Google Gemini API, Hugging Face Transformers, Streamlit, Gradio, FastAPI or Flask Why it matters: This repository provides a platform for experimenting with and demonstrating the potential of generative AI across multiple domains, including text generation, image synthesis, and code creation. Repository: AryanKarumuri/Gen-AI-Projects Primary language: Jupyter Notebook GitHub stars: 6 GitHub description: This repository features a collection of generative AI applications designed to showcase the capabilities of AI across various domains. Explore projects that generate text, images, music, and code, each demonstrating innovative uses of generative technology. Contribute your own projects and experiment with the potential of AI! ## Table of Contents📑 - [Project Overview](#project-overview) - [Technologies Used](#technologies-used) - [API Key Generation Guide](#api-key-generation-guide) - [Deploying on Streamlit](#deploying-on-streamlit) - [Getting Started](#getting-started) - [Contributing](#contributing) - [Note](#note-on-model-output) - [License](#license) ## Project Overview📝 This repository showcases a variety of generative AI applications, each designed to illustrate

Overview: Floor planning using AI-driven models is showcased in this project, leveraging advanced fine-tuned models and pre-trained systems to generate precise and accurate floor plans. Key features: - AI-Generated Floor Plans - Model Fine-Tuning - API Integrations with Janus, Optimized_Cloudqi_Text_to_Image_Model, and Vertex AI - Custom Safety Mechanisms - Performance Optimization Tech stack: Jupyter Notebook Why it matters: This project demonstrates the application of AI in architectural design, showcasing how AI can enhance the accuracy and efficiency of floor plan generation, making it a valuable tool for architects and designers. Repository: Auth0r-C0dez/Neurathon-25 Primary language: Jupyter Notebook GitHub stars: 6 GitHub description: This is the project for the final round of neurathon 2025

Overview: SOLLOL is a performance-aware load balancer and orchestration layer designed for distributed Ollama deployments. It routes requests to the best available node based on GPU/CPU capacity and current load, ensuring optimal performance and reliability. Key features: - Intelligent routing that scores nodes based on success rate, latency, GPU availability, current load, priority, and task type. - Auto-discovery of Ollama nodes on the network. - GPU-aware routing to avoid routing heavy models to nodes without the necessary GPU. - Distributed execution with Ray for parallel request execution and Dask for batch processing. - Live dashboard for monitoring node health, latency, active applications, and GPU memory usage. Tech stack: Python, Ray, Dask, Redis, and Ollama. Why it matters: SOLLOL simplifies the management of distributed Ollama clusters, ensuring optimal performance and reliability through intelligent routing and observability. Repository: B-A-M-N/SOLLOL GitHub stars: 6 GitHub description: Super Ollama Load Balancer & Orchestration Layer

Overview: This project involves developing a multilingual LLM prompt-attack classifier for the RMIT SGS GenAI & Cybersecurity Hackathon 2026 competition. The classifier is fine-tuned on mmBERT-base and QLoRA-tuned Granite-2B, achieving a macro F1 score of 0.98241. The classifier classifies prompts as benign, jailbreak, or prompt injection, with a focus on fine-tuning models from the approved list and using 5-fold cross-validation for validation. Key features: The classifier is fine-tuned on mmBERT-base and QLoRA-tuned Granite-2B, achieving a macro F1 score of 0.98241. It classifies prompts as benign, jailbreak, or prompt injection, with a focus on fine-tuning models from the approved list and using 5-fold cross-validation for validation. Tech stack: The project uses Jupyter Notebook and fine-tuned models from the approved list, including mmBERT-base, QLoRA-tuned Granite-2B, and 60+ fine-tuning runs on 9 backbones. Why it matters: The project showcases the effectiveness of fine-tuning models and the importance of cross-validation in improving model performance, as demonstrated by the high private macro F1 score of 0.98241, which is 0.0036 better than the public score of 0.98111.

Overview: This repository contains 70 hands-on projects designed to help developers enhance their skills in Generative AI and related technologies. Ideal for beginners and experienced professionals alike, the projects cover a wide range of topics and provide practical applications. Key features: - Hands-On Projects: Over 70 projects covering various AI topics. - User-Friendly: No coding skills required. The platform guides you through each project. - Resource Materials: Includes detailed guides and Jupyter notebooks for reference. - Community Support: Join community forums for help and collaboration. Tech stack: Python, Jupyter Notebook, and other relevant tools. Why it matters: This resource is essential for anyone looking to improve their skills in AI and related technologies, providing practical experiences that can be directly applied in real-world projects. GitHub stars: 6 Repository: Brucewaynehatake/GenAI-Architect-70-Hands-On-Projects

### Overview: This project is a Khmer-language math AI solution for Cambodia's BacII exam, offering 108,332 worked solutions across 8 chapters. The model is fine-tuned on a Gemma 3 12B model and verified by SymPy, providing accurate and detailed answers in Khmer. ### Key features: - 108,332 worked solutions in Khmer, covering 8 chapters of the BacII exam. - Every answer is computed by SymPy, ensuring no language model wrote a solution. - A LoRA adapter trained on Gemma 3 12B model, fine-tuned for Khmer BacII style. - A local chat page that automatically checks answers against the dataset or SymPy. ### Tech stack: Python ### Why it matters: This project offers a comprehensive and accurate math solution for students preparing for the Cambodian BacII exam, ensuring they have access to detailed and verified answers.

### Overview: Reson is a fine-tuned version of LLaMA-2 7B HF, trained on a custom dataset of ~11,000 examples. It is designed as a decision-support and cognitive simulation system, focusing on extreme meta-cognition, multi-domain reasoning, and strategic adaptability. ### Key Features: - Multi-domain reasoning - Extreme meta-cognition - Strategic adaptability - Unconventional logic blending - Exploratory cognition ### Tech Stack: Python ### Why it Matters: Reson is a powerful tool for exploring complex cognitive processes and adaptive reasoning in a wide range of domains, pushing the boundaries of what traditional chatbots can achieve.

### Overview: DenseCore is a CPU inference server designed for large language models (LLMs) using the GGUF format. It provides a native C++ runtime and OpenAI-compatible API, making it suitable for high-performance inference on plain CPUs. ### Key Features: - **Native C++ Runtime**: Executes the GGUF model in native C++. - **OpenAI-Compatible API**: Supports chat and prompt completions, embeddings, and more. - **Production Readiness**: Includes readiness/liveness probes, Prometheus metrics, and bounded admission. - **Docker Image**: Provides a production-ready Docker container. - **Python SDK and Helm Chart**: Offers a Python SDK and a Helm chart for deployment. - **Typed Decisions**: Supports Laya GGUF with a native System One API (preview). - **Hardware Support**: Available for Linux x86-64, Linux Arm64, and experimental Apple and accelerator paths. ### Tech Stack: C++ and Go. ### Why It Matters: DenseCore significantly improves the performance of LLM inference on CPUs, achieving up to 39% faster first token latency compared to llama.cpp. This makes it ideal for applications requiring high throughput and low latency. ### Repository: Repository: [DenseAI/DenseCore](https://github.com/DenseAI/DenseCore) GitHub Stars: 6 GitHub Description: CPU inference server for GGUF MoE models.

Overview: LangBatch is a unified API for accessing Batch APIs from providers like OpenAI, Anthropic, Azure OpenAI, Vertex AI, and Bedrock, offering a streamlined approach to handling batch jobs and requests. Key features: - OpenAI format for requests and responses - Utilities for managing batch jobs: creation, starting, monitoring, retrying, and processing - Pipeline to convert incoming requests into batch jobs Tech stack: Python Why it matters: LangBatch simplifies the process of interacting with multiple Batch APIs, providing a single interface for developers to work with, thereby reducing complexity and improving efficiency. Repository: EasyLLM/langbatch Primary language: Python GitHub stars: 6 GitHub description: LangBatch is a unified API for accessing Batch APIs from providers like OpenAI, Anthropic, Azure OpenAI, Vertex AI, and Bedrock, offering a streamlined approach to handling batch jobs and requests.

Overview: Fine-tuning Microsoft Phi-4 Mini 3.8B on SEC 10-K financial Q&A using QLoRA — +69% ROUGE-L over base model. Live demo on Hugging Face. Key features: - Fine-tuning of Microsoft Phi-4 Mini 3.8B - Training on free Kaggle GPUs - 69% improvement in ROUGE-L over base model - Live demo on Hugging Face Spaces Tech stack: Jupyter Notebook Why it matters: This project fine-tunes a powerful language model on financial data to provide insights that are more accurate and relevant for financial analysts, making it a valuable tool for the finance industry. Repository: Emart29/phi4-finance-finetuning