Projects

Overview: This project involves fine-tuning the Gemma-2b-it model specifically for generating code. Key features: - Fine-tuning Gemma-2b-it for generating code - Utilizing low-rank adapters (LoRA) with QLoRA parameters - Training on the TokenBender/code_instructions_122k_alpaca_style dataset - Preview of the model's performance before and after fine-tuning Tech stack: Jupyter Notebook Why it matters: This project showcases the potential of fine-tuning large language models for practical applications, such as generating code, and demonstrates the effectiveness of low-rank adapters in enhancing model performance.

### Overview: Nano Banana 2 Lite Prompts: 8 Tested Recipes with Outputs ### Key Features: - **Model ID:** `gemini-3.1-flash-lite-image-ext` - **Price:** $0.0125 per image - **Cost:** Estimated cost of the whole set: $0.1 ### Tech Stack: Python ### Why It Matters: Nano Banana 2 Lite (`gemini-3.1-flash-lite-image-ext`) provides a cost-effective and OpenAI-compatible endpoint for image prompts, allowing for efficient and scalable image generation without the need for a monthly subscription. The API is compatible with various models and supports multiple aspects such as aspect ratios, lens language, and rendering techniques, ensuring high-quality results at a competitive price.

### Overview: UltraMemory is a universal AI agent long-term memory engine that leverages hybrid vector retrieval, cross-encoder reranking, Weibull decay, LLM smart extraction, semantic deduplication, and L0/L1/L2 tiered content storage. It is designed for zero-ops LanceDB backend, making it highly efficient and scalable. ### Key Features: - **Hybrid Vector+BM25 Retrieval**: Combines vector-based and BM25 search for enhanced relevance. - **Cross-Encoder Reranking**: Improves recall by reranking documents based on cross-encoder scores. - **Weibull Decay**: Maintains memory freshness with a Weibull decay algorithm. - **LLM Smart Extraction**: Extracts meaningful information from large language models. - **Semantic Deduplication**: Ensures unique and accurate memory representation. - **Tiered Content Storage**: Stores content in L0, L1, and L2 layers for efficient recall. - **Zero-ops LanceDB Backend**: Efficiently stores and retrieves data without external dependencies. ### Tech Stack: TypeScript ### Why It Matters: UltraMemory is essential for agents that need to remember and recall information across sessions, agents, and time. Its advanced features make it highly competitive in the field of AI memory engines, ensuring efficient and effective memory management. ### Repository: win4r/UltraMemory ### GitHub Stars: 7 ### GitHub Description: Universal AI Agent Long-Term Memory Engine — Hybrid Vector+BM25, cross-encoder reranking, Weibull decay, L

Overview: Seedance 2.0 Fast API is a model id, pricing, and parameterized API for the OpenAI-compatible APIMart endpoint, providing fast and efficient image and video generation. Key features: - Model id: Doubao-seedance-2.0-fast - Pricing: Per generated second, with resolution-based pricing - Parameters: Model, prompt, duration, resolution, size/aspect ratio, image URLs, and seed - Billing: $0.0213 per second - API key required for access Tech stack: Python Why it matters: Seedance 2.0 Fast API simplifies the process of generating high-quality images and videos with OpenAI-compatible models, offering fast performance and easy integration. Repository: wlpm01/seedance-2-0-fast-api-ru Primary language: Python GitHub stars: 7 GitHub description: Seedance 2.0 Fast API (`doubao-seedance-2.0-fast`): model id, pricing, parameters and copy-paste examples for the OpenAI-compatible APIMart endpoint. README excerpt: <!-- conv-kit:v1 --> <p align="center"> <img src="assets/badges/price.svg" alt="observed unit price"> <img src="assets/badges/billing.svg" alt="billing model"> <img src="assets/badges/compat.svg" alt="OpenAI-compatible endpoint"> </p> <p align="center"> <img src="assets/01-product-ad-ceramic

### Overview: FLUX.2 Flex prompts offer tested image prompt recipes with detailed example renders, aspect ratios, and the model ID (`flux-2-flex`) and cost per image. This repository provides a concise guide on how to optimize image prompts effectively. ### Key Features: - **Prompt Recipes:** Eight tested recipes with detailed renders, aspect ratios, and the cost per image. - **Model ID:** `flux-2-flex` for access to 300+ image, video, and language models. - **Cost Estimation:** $0.04 per image, with a total estimated cost of $0.32 for the set. - **API Key:** Access to the APIMart API for $0.04 per image, with live pricing and model page. ### Tech Stack: - Python ### Why It Matters: - **Key Features:** The recipes are tested and optimized for effective image rendering. - **Model ID:** `flux-2-flex` provides access to a wide range of models, including 300+ image, video, and language models. - **Cost Estimation:** The cost is estimated at $0.04 per image, with a total estimated cost of $0.32 for the set. - **API Key:** Access to the APIMart API for $0.04 per image, with live pricing and model page. ### Gallery: Each row represents a recipe with an example render, category, aspect ratio, cost on this route, and the exact prompt.

Overview: GPT Image 1.5 API is a model id, pricing, and parameterized endpoint for the OpenAI-compatible APIMart endpoint, providing access to a wide range of image and video models. Key features: - Supports multiple image and video models - Offers token-based billing with flexible pricing tiers - Provides API key and live pricing information - Asynchronous processing with task tracking Tech stack: Python Why it matters: This API allows developers to easily integrate OpenAI-compatible image generation into their projects without the need for a subscription, making it ideal for applications requiring image manipulation without the cost of a monthly plan. Repository: x58ngjiip5d/gpt-image-1-5-api-id GitHub stars: 7 GitHub description: GPT Image 1.5 API (`gpt-image-1.5-official`): model id, pricing, parameters and copy-paste examples for the OpenAI-compatible APIMart endpoint.

Overview: DataFountain训练赛道赛题“天气以及时间分类”解决方案，测试成绩为0.94934795，第二赛段排行榜A榜排名第一。 Key features: - 使用迁移学习和多模型投票的方法进行天气和时间分类。 - 去除了训练集中未出现的三种类别，将剩余的“Dawn”、“Morning”、“Afternoon”和“Dusk”与“Cloudy”、“Sunny”和“Rainy”合并为12类进行分类。 - 所有经过预训练的模型均在ImageNet上完成预训练。 - 模型参数按网络结构分类并存放在voting_models文件夹下，用于投票过程。 Tech stack: Python Why it matters: 该解决方案在DataFountain训练赛道赛题中取得了优异的成绩，特别是在天气和时间分类方面，展示了强大的分类能力。

### Overview: Gemini Nano Banana 2.1 prompts offer tested image recipe suggestions for various applications, including product photography, food photography, and more. The prompts are designed to guide users in creating high-quality images with minimal effort. ### Key Features: - **Prompt Structure:** Provides a clear and concise structure for creating effective image prompts. - **Model ID:** `gemini-nano-banana-2.1` - **Costs:** $0.0137 per image, estimated cost for the set: $0.1096 ### Tech Stack: - Python ### Why It Matters: - **Efficiency:** Users can quickly access the Gemini Nano Banana 2.1 model with a single API key, eliminating the need for a monthly subscription. - **Cost-Effectiveness:** The cost is minimal, making it accessible for small-scale projects. - **Quality Control:** The prompts are designed to guide users in creating high-quality images, reducing the guesswork and ensuring consistent results. ### Repository: xou-fm9md3t/gemini-nano-banana-2-1-prompts-ko ### Primary Language: Python ### GitHub Stars: 7 ### GitHub Description: 나노바나나 / 나노 바나나 — Gemini Nano Banana 2.1 prompts: tested image prompt recipes with example renders, aspect ratios, the model id (`gemini-nano-banana-2.1`) and the cost per image.

### Overview: eqx-zoo is a project that provides verified JAX/Equinox ports of Hugging Face language models and embedding models. These models are numerically matched against PyTorch, forward and backward, and saved in Hugging Face formats. The repository includes pretrained models as Equinox modules, making them compatible with the JAX ecosystem. ### Key features: - Pretrained models in Equinox format - Numerically verified against PyTorch - Saved in Hugging Face formats - Compatible with JAX ecosystem ### Tech stack: Equinox ### Why it matters: eqx-zoo provides a verified way to use Hugging Face models in JAX, ensuring compatibility and performance. It allows developers to leverage the latest advancements in JAX while maintaining the accuracy and reliability of their models.

Overview: ATLAS-TQ1.0 is a CPU-only ternary inference engine designed for 1.58-bit quantized LLMs. It packs the Falcon3-1B model at 10.1 tok/s on a DDR4 system. Key features: - TQ1.0 Packer (`pack_to_atlas.py`): Converts HuggingFace safetensors to TQ1.0 format. - 5 matmul modes: int8, int4, f32 bypass, ternary, TQ1-packed. - C API and C++ CLI. - Streaming SSE Server (`atlas_server.py`). - DeepSeek-V2 latent attention and sparse mixture-of-experts (v2.17.0). - Ported all 8 hot-path kernels to ARM64 NEON. - Binary Tokenizer v6 format, no transformers dependency. Tech stack: C++. Why it matters: ATLAS-TQ1.0 is the only production-grade format for ternary 1.58-bit quantization, providing a performance boost for 7B models on DDR4 systems. Repository: xxxn3m3s1sxxx/ATLAS-TQ1_0 GitHub stars: 7 GitHub description: TQ1.0 ternary inference engine for BitNet b1.58 on CPU. Pack + run Falcon3-1B/3B/7B/10B, no GPU needed. License: Apache 2.0.

Overview: This repository contains eight tested Wan 2.7 Image prompts, each accompanied by an example render, aspect ratio, and the cost of the call on the Wan 2.7 Image model (`wan2.7-image`). The prompts are designed to be used with the APIMart API, which offers an OpenAI-compatible endpoint for a cost of $0.0216 per image, without the need for a monthly subscription or prepaid plans. Key features: - Eight Wan 2.7 Image prompts - Example renders, aspect ratios, and call costs - Model: `wan2.7-image` - Prompt structure cheat-sheet Tech stack: Python Why it matters: This set of prompts provides a practical guide for creating high-quality images using the Wan 2.7 Image model, ensuring that the output is tailored to the specific needs of the project while maintaining a cost-effective approach to image generation.

### Overview: GeoGuessr Location Assistant is a project that focuses on developing a street-level image analysis pipeline for geographic location inference using YOLO object detection, Optical Character Recognition (OCR), and Overpass/Nominatim queries. The project aims to achieve high accuracy in predicting the correct city from street-level images, leveraging advanced machine learning techniques and geographic data sources. ### Key Features: - **YOLOv8 Detection**: Utilizes YOLOv8 for object detection. - **OCR Integration**: Integrates OCR to extract text from images. - **Overpass/Nominatim Queries**: Queries OpenStreetMap and Nominatim for geolocation data. - **Hybrid AI Pipeline**: Combines YOLO detections, OCR results, and Overpass/Nominatim queries to predict the correct city. ### Tech Stack: - **end_logic**: YOLOv8 detection, OCR, Overpass/Nominatim queries. - **backend**: FastAPI service exposing the YOLOv8 detection results. - **frontend**: ReactJS application for the chatbot interface. - **data_prep**: Scripts for preparing the dataset for model training. ### Why It Matters: This project is significant because it demonstrates the integration of various machine learning techniques to enhance the accuracy of geographic location inference from street-level images. The use of YOLOv8 for object detection, OCR for text extraction, and Overpass/Nominatim queries for geolocation data provides a robust solution for urban mapping and location inference. ### Repository: - **Repository**: yacine20

Overview: GPT Image 2.5 API is a model id, pricing, and parameters for the OpenAI-compatible APIMart endpoint, providing access to various image and video models. Key features: - Support for OpenAI's image models on the flare and sunburst routes, priced per delivered image by resolution. - Pricing details including per-image rates for different resolutions. - Asynchronous execution from the start. - Billing based on actual usage, with cost reported in the response. Tech stack: Python Why it matters: This API allows developers to use OpenAI's models for image generation, providing a convenient and efficient way to integrate advanced AI capabilities into their projects without the need for a subscription. Repository: ylpn4zf3x25/gpt-image-2.5-api-id Primary language: Python GitHub stars: 7

Overview: ComfyUI_RH_VoxCPM is a developer-friendly tool that integrates VoxCPM nodes into the ComfyUI workflow, enabling realistic speech synthesis and voice cloning directly within the ComfyUI environment. This plugin simplifies the process of processing 3D data and voxel models, making it easier to integrate advanced voice synthesis capabilities into existing projects. Key features: - Realistic speech synthesis - Voice cloning - Token-free TTS - Style guidance - LoRA training support Tech stack: Python Why it matters: This plugin streamlines the integration of advanced voice synthesis technology into the ComfyUI ecosystem, making it easier for developers to create more immersive and realistic voice-based applications.

### Overview: X-Forge Factory is a self-hosted operator console for Magnific, providing access to all capabilities on a single surface. It integrates with both the REST API and MCP servers, offering 48 image models, 52 video models, and a credit-safe job engine. ### Key Features: - **REST API and MCP Integration:** Connects to Magnific's REST API and MCP server via OAuth. - **Credit-Safe Job Engine:** Manages credits as if they were money, ensuring charges are accurate and credits are reserved before work starts. - **Full Catalogue Access:** Provides access to all 48 image models, 52 video models, and over 600 voices. - **Text-to-Speech and 3D Features:** Additional capabilities beyond the standard Magnific features. - **Dashboard Interface:** Simplifies the console's interface for easy navigation and management. ### Tech Stack: TypeScript ### Why It Matters: X-Forge Factory offers a unified interface for all Magnific capabilities, ensuring accurate cost management and access to a comprehensive catalog. The job engine's credit-safety feature ensures that credits are treated as valuable resources, enhancing the overall user experience. Repository: yuriiss/X-Forge-Factory GitHub Stars: 7 GitHub Description: Self-hosted operator console for Magnific / Freepik AI. REST + MCP, 48 image models, 52 video models, credit-safe job engine

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.