| | --- |
| | title: Celeste Imperia | Hardware-Aware AI Forge |
| | emoji: 🌌 |
| | colorFrom: blue |
| | colorTo: purple |
| | sdk: static |
| | pinned: true |
| | thumbnail: >- |
| | https://cdn-uploads.huggingface.co/production/uploads/697f74cb005a67fc114da2b4/OCwnTMS-RIt9cPQR0Gnxf.png |
| | license: apache-2.0 |
| | tags: |
| | - edge-ai |
| | - openvino |
| | - whisper |
| | - sdxl |
| | - quantization |
| | - windows-on-arm |
| | - itsabhishek19 |
| | --- |
| | |
| | # 🗺️ Celeste Imperia: The Forge Guide |
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| | Welcome to the Forge. This guide is designed to help you navigate our optimized model suite and choose the perfect "flavor" for your specific hardware. |
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| | Whether you are running a high-end workstation or a 4GB RAM laptop, we have a build for you. |
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| | --- |
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| | ## 🏎️ 1. Choose Your Engine (Format) |
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| | We provide models in two primary formats, each optimized for different ecosystems: |
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| | ### **OpenVINO (Intel Optimized)** |
| | Best for Windows and Linux systems with **Intel Core** processors or **Intel ARC/Iris** graphics. These models leverage the `optimum-intel` library for maximum hardware utilization. |
| | * **Use case:** High-speed local image generation and real-time speech-to-text. |
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| | ### **GGUF (Universal CPU)** |
| | The industry standard for "run-anywhere" AI. These models are designed for `llama.cpp` and work seamlessly on **Apple Silicon (M1/M2/M3)**, **AMD**, and **Snapdragon** devices. |
| | * **Use case:** Large Language Models (LLMs) running in private, low-resource environments. |
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| | --- |
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| | ## 💎 2. Choose Your Precision (The Trinity) |
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| | We categorize our models into three tiers. Use the table below to find your match: |
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| | | Tier | Precision | Hardware Requirement | Best For... | |
| | | :--- | :--- | :--- | :--- | |
| | | **Master** | `FP16` | 32GB+ RAM / 16GB VRAM | **Production Quality:** No loss in detail. Best for professional creative work. | |
| | | **Pro** | `INT8` | 16GB RAM | **Daily Driving:** 50% smaller size with ~99% quality retention. Perfectly balanced. | |
| | | **Lite** | `INT4` | 8GB RAM / Laptops | **Maximum Speed:** The smallest possible footprint. Ideal for background tasks and edge devices. | |
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| | --- |
| |
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| | ## 🚀 3. Hardware Recommendation Matrix |
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| | ### **The Laptop Setup (Lite/Mobile)** |
| | * **Target:** 8GB RAM / Intel i5 (10th Gen+) |
| | * **Recommendation:** Use our **INT4 (Lite)** OpenVINO models or **Q4_K_M** GGUF weights. |
| | * **Result:** Fast, snappy responses without freezing your system. |
| |
|
| | ### **The Creator Setup (Pro/Standard)** |
| | * **Target:** 16GB - 32GB RAM / Intel i7 or i9 |
| | * **Recommendation:** Use our **INT8 (Pro)** OpenVINO models for SDXL and **Q8_0** for LLMs. |
| | * **Result:** Professional-grade outputs with lightning-fast inference. |
| | |
| | ### **The Workstation Setup (Master)** |
| | * **Target:** 64GB RAM / Intel ARC A770 or RTX 4000 |
| | * **Recommendation:** Use our **FP16 (Master)** suite. |
| | * **Result:** Zero-compromise AI at maximum hardware throughput. |
| | |
| | --- |
| | |
| | ## 🛠️ Quick Performance Tips |
| | |
| | 1. **The "First Run" Tax:** The very first time you run an OpenVINO model, it will take 30-60 seconds to compile the graph. **Don't cancel it.** Every run after that will be nearly instant. |
| | 2. **Guidance Scale:** For our **SDXL Trinity**, always keep your `guidance_scale` between **1.0 and 2.0**. We use fused LCM technology, and high CFG values will cause artifacts. |
| | 3. **Background Tasks:** AI inference is CPU-heavy. For the fastest results, close memory-heavy apps like Chrome or Photoshop before starting a large generation. |
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| | --- |
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| | **Need more help?** Check out our individual repository [Model Cards](https://huggingface.co/CelesteImperia) for specific implementation code. |
