Projects

### Overview: Stratum is a pure-C transformer inference engine designed specifically for Apple Silicon. It leverages wired memory independence, allowing the model to run efficiently on the device without requiring significant RAM. The engine is lightweight, with a binary size of approximately 752 KB, making it ideal for applications that require low memory footprint. ### Key Features: - **Weight Management**: The model stores weights in a file, allowing for efficient anonymous memory usage. - **Hot and Cold Layers**: The engine runs in two layers: a hot layer for immediate computation and a cold layer for prefetching. - **Multi-Stream Decode**: Supports up to 64 decode streams, enhancing throughput. - **Dense and Hybrid Architectures**: Supports both dense and hybrid (MoE) architectures, with experimental MoE support. - **Metal GPU Registration**: A low-confidence draft can be skipped to improve efficiency. ### Tech Stack: - **Language**: C - **Platform**: Apple Silicon - **Dependencies**: `metal` for Metal support, `metal` for Metal support, `metal` for Metal support ### Why It Matters: - **Efficiency**: The engine runs efficiently on Apple Silicon, utilizing wired memory to keep the model lightweight. - **Scalability**: Supports up to 64 decode streams, making it suitable for high-throughput applications. - **Performance**: Measured on an M4 Pro, the engine can achieve impressive throughput, particularly with the 27B model, which runs in just 77 MB of anonymous memory. ### Tech Stack: - **

### Overview: Rectified-CFG++ is a training-free, geometry-aware guidance scheme designed for flow-based text-to-image (T2I) models. It replaces the naive extrapolation of classifier-free guidance (CFG) with a predictor-corrector integrator that stays on the learned data manifold, eliminating structural artifacts while improving prompt alignment and over-generation quality. ### Key Features: - **On-Manifold Sampling**: Predictor-corrector updates keep trajectories on the data manifold. - **Training-Free**: Drop-in replacement for standard CFG. No additional training or fine-tuning required. - **Model-Agnostic**: Works with any transformer-based rectified-flow T2I backbone (e.g., Flux, SD3/3.5, Lumina). - **Stable Across Scales**: Maintains visual fidelity and prompt alignment even at high guidance strengths. - **Efficient**: Achieves state-of-the-art FID and CLIP-Score. ### Tech Stack: Python ### Why It Matters: Rectified-CFG++ is a significant advancement in the field of flow-based T2I models, offering improved prompt alignment, visual quality, and stability across various scales without the need for additional training. It is particularly useful for enhancing the quality of generated images in applications such as art generation and creative design. ### Repository: shreshthsaini/Rectified-CFGpp ### GitHub Stars: 11 ### GitHub Description: Repository for Rectified-CFG++ Project. ### GitHub License: MIT License ### Installation: 1. Clone the repository: ```bash git

Overview: This repository contains a system for generating question-answer pairs for fine-tuning Large Language Models (LLMs) using the provided data. It leverages various modules to extract text, generate questions, and save the generated questions. Key features: - Automatically processes HTML as input files. - Removes duplicate and similar questions. - Simplifies the JSONL output format cleaning process. Tech stack: Python Why it matters: This system is essential for improving the performance and accuracy of LLMs by fine-tuning them with relevant and diverse question-answer pairs derived from existing data.

Overview: This repository contains a community-maintained Docker image optimized for Intel Arc Pro B70, specifically targeting the B70 (Battlemage B-series) cards. The image includes the latest version of `llama.cpp` with SYCL support, ensuring all features are enabled, including Flash Attention, MTP, reorder kernels, and no `GGML_SYCL_DISABLE_OPT`. Key features: - The image is built with the latest oneAPI/compute-runtime/IGC, ensuring compatibility and performance. - It includes a golden configuration verified against v0.4.1, showcasing high performance and reliability. - The image is designed to reliably complete a full agent suite on B70, including tasks T1–T5, with a prefill speed of 470–490 t/s and decode speed of 41–46 t/s. - It supports the Q8_0 KV with 128k context and MTP3, making it compatible with the golden configuration. Tech stack: SYCL + oneAPI/compute-runtime/IGC Why it matters: This image is crucial for developers looking to optimize their performance on Intel Arc Pro B70 cards, ensuring compatibility with the latest Intel Compute Runtime and IGC, while maintaining the highest performance and reliability. Repository: snailium/llama.cpp-sycl-intel-b70 GitHub stars: 11

### Overview: Winston is an advanced AI Legal Defense System powered by 805,000+ legal documents from the entire US legal system. It features 31 specialized Slack commands, voice support, session memory, and smart caching, making it a comprehensive tool for legal professionals and individuals seeking legal assistance. ### Key Features: - **31 Specialized Slash Commands**: Covers various legal topics such as law enforcement, tax law, contracts, and more. - **805,000+ Legal Documents**: Comprehensive coverage of the entire US legal system. - **Voice Message Support**: Whisper AI transcription for enhanced communication. - **Session Memory**: 24-hour conversation history for continuous legal advice. - **Smart Caching**: 98% semantic similarity matching for accurate responses. - **Production Ready**: Includes health checks, metrics, logging, and rate limiting for reliable operation. ### Tech Stack: Built with Agent OS, Claude-Flow, and Claude Code, this system leverages cutting-edge AI technologies to provide accurate and efficient legal assistance. ### Why It Matters: Winston is designed to be a go-to solution for anyone seeking legal advice, ensuring that users can access the necessary information quickly and efficiently. Its comprehensive coverage and advanced features make it a valuable tool for both professionals and the general public. ### Repository: The repository is hosted on GitHub at [spinbased/Winston](https://github.com/spinbased/Winston). It has received 11 GitHub stars, indicating its popularity and usefulness among developers and users. ### Primary Language

### Overview: This repository provides a concise explanation of various concepts in Large Language Models (LLMs) in 10 words or less. ### Key features: - **Attention:** Weighs token representations based on relevance. - **Multi-Head Attention (MHA):** One key-value head per query head. - **Multi-Query Attention (MQA):** One key-value head shared by all query heads. - **Grouped-Query Attention (GQA):** One key-value head shared by each query-head group. - **Multi-Head Latent Attention (MLA):** One compressed key-value representation shared by all query heads. - **Residual Connections:** Add earlier representations back into later layers. - **Attention Residuals:** Attend over outputs from all earlier layers. - **Kimi Delta Attention (KDA):** Store compact delta-updated memory instead of full key-value history. - **Compressed Sparse Attention 2 (CSA2):** Reuse key-value across layers, occasionally refreshing cached tokens. - **Causal Encoder-Decoder (CED):** Use encoder hidden states for decoder key-value, halving prefill compute. - **Mixture of Experts (MoE):** Routes each token through a subset of experts. - **Latent MoE:** Compress tokens before expert routing and computation. - **Rotary Positional Embeddings (RoPE):** Encode token position by rotating query and key vectors. - **Looped Transformers:** Reuse transformer layers; same weights, more thinking. - **Training Methods:**

### Overview: Cascade is a project that extends LLM context windows beyond the limitations of GPU memory by utilizing a disk-backed key-value cache tier. This approach allows inference engines to run on local NVMe storage, reducing dependency on expensive GPU HBM memory. ### Key features: - **Disk-backed KV cache**: Keeps the inference engine API small, moving metadata and eviction into a compact Go service. - **POSIX backend**: Supports portable CPU-bounce-buffer storage. - **GPUDirect Storage (GDS) backend**: Enables GPU↔NVMe transfer on supported hosts. - **Cluster roadmap**: Plans for RDMA-accessible pooled SSD and GPU-aware scheduling. ### Tech stack: - Python vLLM connector for saving and loading KV tensors through pluggable storage backends. ### Why it matters: - **Make long-context inference cheaper and more elastic without changing the model**.

### Overview: Nano Banana 2 Lite Prompts offers tested image prompt recipes for various subjects, including product photography, food photography, and more. The recipes are designed to guide users on how to craft effective prompts for Nano Banana 2 Lite, a model compatible with the OpenAI API. ### Key Features: - **Model ID:** `gemini-3.1-flash-lite-image-ext` - **Aspect Ratios:** 1:1, 4:3, 3:4, 8:5, etc. - **Cost Per Image:** $0.0125 - **Example Renders:** Provided in the README and `data/prompts.json` file. ### Tech Stack: - Python ### Why It Matters: Nano Banana 2 Lite Prompts provides a straightforward way for developers to create effective image prompts using the Nano Banana 2 Lite model, ensuring that the output meets the desired quality and context. The API is cost-effective, with the cost per image listed, making it ideal for teams looking to optimize their image generation processes without the need for a monthly subscription.

Overview: This CLI allows developers to easily render text and HTML to images using headless Chrome, providing a robust and modern solution to the traditional image rendering challenges. Key features: - Built-in font face observer - Easy loading of Google fonts - Optional word wrap - Main content is just HTML - Styling is done via CSS - Handles multiple fonts - Thoroughly tested - Includes a CLI Tech stack: JavaScript Why it matters: This CLI simplifies the process of rendering text and HTML to images using headless Chrome, offering a modern and efficient alternative to traditional image rendering tools.

### Overview: Grok Imagine 1.5 API is a powerful tool for generating images, videos, and language models using the OpenAI-compatible APIMart endpoint. It offers a single API key that can be used to access over 300 models, including Grok Imagine 1.5, without any monthly subscription. The pricing is $0.015 per image, and the API is designed to be highly efficient with minimal setup required. ### Key Features: - **Single API Key**: Access to Grok Imagine 1.5 and over 300 models using a single API key. - **Comprehensive Pricing**: $0.015 per image, with no need for a monthly subscription. - **Model ID and Routes**: The API is priced per image, with resolutions ranging from 1K to 4K. - **Pricing Details**: The table lists the fields accepted by the APIMart `images/generations` endpoint, and the effective price is $0.015 per image. - **Request Parameters**: The API supports parameters such as `model`, `prompt`, `size`, `resolution`, `n`, `image_urls`, and `nsfw_check`. ### Tech Stack: - Python ### Why It Matters: - **Cost Efficiency**: The API is priced per image, making it highly cost-effective for projects that require frequent image generation. - **Model Diversity**: Access to over 300 models ensures that users can choose the best model for their needs, including Grok Imagine 1.5.

### Overview: Nano Banana 2 API, also known as `gemini-3.1-flash-image-preview`, is an OpenAI-compatible API endpoint that offers fast AI image generation with native 4K output and editing capabilities. The API is designed to be highly flexible, allowing teams to switch between different models without changing their client configuration. It supports a variety of pricing tiers and offers a pay-as-you-go model, with the cost of each call reported in the response. ### Key Features: - **One Key, Entire Catalog**: Access to the same `https://api.apimart.ai/v1` base URL and `Authorization` header to reach Nano Banana 2 and 300+ other image, video, and language models. - **$1 Minimum, Pay as You Go**: No subscription or prepaid plan required; top up from $1 and spend it on calls. There is no free quota to burn through first, so the price in this table is the price you pay. - **The Charge Comes Back in the Response**: Every call reports the amount billed (`cost` / `credits_cost`), so a spend number is read per call instead of guessed at month end. - **Async by Design**: Submit, take the `task_id`, and poll `GET /v1/tasks/{id}` — batching and retries are ordinary queue work, not a bespoke integration. ### Tech Stack: - **Python** - The primary language used for this API. ### Why It Matters: - **One Key, Entire Catalog**: The same `https://api.apimart.ai

### Overview: Nano Banana 2 Prompts: A Comprehensive Guide to Image Prompt Recipes Nano Banana 2 Prompts offers a tested and effective way to generate high-quality images using the Gemini-3.1-flash-image-preview model. With a cost of $0.015 per image, this service is both affordable and flexible, allowing users to pay as they go without the need for a monthly subscription. The API is compatible with OpenAI endpoints, making it accessible to a wide range of applications. ### Key Features: - **Model Compatibility**: Works seamlessly with the Gemini-3.1-flash-image-preview model, offering a consistent and reliable output. - **Cost Efficiency**: The cost is calculated directly in the response, providing an accurate estimate of the charges per image. - **Model ID**: Utilizes the model id `gemini-3.1-flash-image-preview` for precise control and performance. - **Aspect Ratios**: The prompt structure is designed to be flexible, allowing for various aspect ratios like 1:1, 4:3, and more. - **Prompt Structure**: The prompt template is provided in both Markdown and JSON formats, making it easy to copy and modify. ### Tech Stack: OpenAI-compatible Endpoint Nano Banana 2 Prompts leverages an OpenAI-compatible endpoint, ensuring compatibility with a variety of models and applications. ### Why It Matters: - **Consistency**: The prompts are designed to be consistent across different models and applications, ensuring uniformity in the output. - **Efficiency**: The service is efficient in terms of cost and performance

### Overview: Nano Banana Prompts is a collection of 8 tested image prompt recipes, each accompanied by an example render, aspect ratio, and the model ID (`gemini-2.5-flash-image-preview`) and cost per image on this route. ### Key Features: - **Model ID:** `gemini-2.5-flash-image-preview` - **Recipes:** 8 machine-readable prompts in JSON format and a table in Markdown. - **Effective Price:** $0.0125 per image, estimated cost for the entire set: $0.1. ### Tech Stack: - Python ### Why It Matters: - **Cost Efficiency:** Nano Banana prompts offer a cost-effective alternative to traditional image generation, with a minimal $1 cost per image. - **Model Compatibility:** Accessible via a single base URL and `Authorization` header, Nano Banana can be integrated into various applications without the need for a monthly subscription. - **Clear Instructions:** The prompts follow a consistent structure, making it easy to understand and implement. - **Performance:** The prompts are designed to yield high-quality results without the guesswork often associated with machine learning models. ### Team's Use Case: - **Nano Banana Prompts** have been successfully used by teams to generate high-quality images for product catalogs, e-commerce, and editorial photography without the need for a monthly subscription. ### Additional Information: - **Alternative Names:** Nano Banana, Nano Barana - **Example Renders:** - Product / e-commerce: A frosted glass perfume bottle on a wet black stone slab. - Food

### Overview: Explore 8 tested Qwen Image 2.0 prompts with examples, aspect ratios, and the model ID. This repository provides insights into the effective pricing and usage of the Qwen Image 2.0 model. ### Key Features: - **Model ID:** `qwen-image-2.0` - **Prompt Structure:** Provides a cheat-sheet for structuring prompts effectively. - **Aspect Ratios:** 1:1, 4:3, 16:9, 85mm, 24mm, macro, overhead flat lay. - **Costs:** $0.02 per image, effective cost of $0.16 for the set. ### Tech Stack: - Python ### Why It Matters: - **Key:** A single key for accessing Qwen Image 2.0 and all related models. - **Cost:** $0.02 per image, with real-time cost reporting and automatic refunds for failed tasks. - **Async:** Efficient batch processing and retry mechanisms without the need for custom integrations. ### Repository: - **Repository:** `ux7qqrhel/qwen-image-2-prompts-cn` - **Stars:** 11 - **Description:** Prompts for Qwen Image 2.0 with example renders, aspect ratios, and pricing information. ### What Separates a Usable Recipe from a Wasted Call: - **1. Name the Material, Not the Adjective:** "Frosted glass" vs. "beautiful". - **2. Set the Light Explicitly

Overview: This repository contains tested image prompt recipes for the GPT Image 1.5 model, including 8 recipes with example renders, aspect ratios, and the model ID and cost per image. Key features: - 8 tested image prompt recipes - Example renders, aspect ratios, model ID, and cost per image - Model: `gpt-image-1.5-official` - Effective price: $4.00 per image - Estimated cost for the entire set: $32.00 Tech stack: Python Why it matters: This repository provides practical guidance for developers looking to optimize image prompts for the GPT Image 1.5 model, ensuring accurate and cost-effective rendering.

### Overview: Z Image Turbo API (`z-image-turbo`) is a high-speed AI image generation model that allows you to generate stunning images from text prompts with lightning-fast processing and flexible aspect ratios. It supports a variety of models and pricing plans, including a per-image pricing model that starts at $0.01 per image. ### Key Features: - **Model ID and Routes:** Supports multiple models and pricing tiers. - **Pricing:** Prices are per delivered image, with a default price of $0.01 and a prompt extension price of $0.02. - **Pricing Model:** The pricing model is based on the actual cost of the task, with a minimum charge of $1 for the first transaction. - **Flexible Pricing:** Supports a range of aspect ratios and resolutions, with the cost scaling accordingly. - **Model Page:** Provides detailed information about the models and pricing tiers. - **Model ID and Routes:** Supports multiple models and pricing tiers. - **Pricing Model:** The pricing model is based on the actual cost of the task, with a minimum charge of $1 for the first transaction. - **Flexible Pricing:** Supports a range of aspect ratios and resolutions, with the cost scaling accordingly. - **Model Page:** Provides detailed information about the models and pricing tiers. ### Tech Stack: - Python ### Why It Matters: - **One Key, Multiple Models:** A single key can access multiple models and image, video, and language models, with no need to modify the client. - **$1 Start, Pay Per Use:** No

Overview: This repository provides an easy-to-use framework for generating RAG-based answers to multilingual questions, evaluating these answers using various metrics, and conducting LLM-as-a-Judge comparisons. It supports both open-source and closed-source LLMs and is designed for the NAACL 2025 Multilingual Retrieval-Augmented Generation (MIRAGE) benchmark. Key features: - Multilingual RAG answer generation using vLLM and other LLMs. - Evaluation of RAG answers using heuristic features or automatic evaluations. - Conducting LLM-as-a-Judge comparisons and training a Bradley-Terry model. - Training a surrogate judge using heuristic features to bootstrap the LLM-as-a-Judge approach. Tech stack: Python Why it matters: This repository is crucial for researchers and developers interested in evaluating and improving multilingual retrieval augmented generation systems, providing a standardized benchmark and evaluation framework for these systems. Repository: vectara/mirage-bench GitHub stars: 11

Overview: Seedance 1.5 Pro Prompts: A collection of 8 tested recipes for image prompts, including aspect ratios, model IDs, and pricing information. Key features: - 4 Seedance 1.5 Pro prompts with example renders, durations, and costs. - Model ID: `seedance-1.5-pro`. - Effective price: $0.0104 per second on the APIMart route. - Estimated cost for the entire set: $0.0416. Tech stack: Python. Why it matters: This repository provides a practical guide for developers looking to optimize their image prompt recipes, ensuring better results and cost efficiency with Seedance 1.5 Pro. Repository: vi21di4j/doubao-seedance-1-5-pro-prompts-id Primary language: Python GitHub stars: 7

### Overview: This project fine-tunes a tool-calling chat model for various domains using supervised learning with LoRA. The pipeline prepares conversation data, injects deterministic system prompts and tool schemas, filters long sequences, trains a LoRA adapter, and evaluates both perplexity and tool-call quality. ### Key Features: - Fine-tuning of a tool-calling chat model for different domains. - Use of supervised learning with LoRA for training. - Preparation of conversation data and tokenization. - Injection of deterministic system prompts and tool schemas. - Filtering of long sequences to ensure model context. - Training and evaluation metrics including perplexity and tool-call quality. ### Tech Stack: Python ### Why It Matters: This approach provides a robust and efficient method for fine-tuning chat models specifically designed for domain-specific tasks, ensuring high accuracy and practical application in real-world scenarios.

Overview: Seedance 1.5 Pro API provides model id, pricing, parameters, and copy-paste examples for the OpenAI-compatible APIMart endpoint, offering unsubscription-based billing, smooth motion, and precise control. Key features: - Model id and pricing information - Parameters for shot description, duration, resolution, and framing - Asynchronous processing - Billing by generated second, with tiered pricing - Model page for detailed information Tech stack: Python Why it matters: Seedance 1.5 Pro API simplifies the process of using Seedance's AI model with precise control over the shot description, duration, and resolution, while ensuring smooth motion and precise control. The unsubscription-based billing model makes it easy to start and stop the API usage as needed, without the need for a monthly subscription or pre-paid plans. Repository: vo0dayvjw/seedance-1-5-pro-api-fr Primary language: Python GitHub stars: 11