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JoyAI-LLM-Flash

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We are excited to announce that the official JoyAI-LLM-Flash collection by jdopensource now provides a complete lineup of models designed to support diverse hardware environments — from high-performance GPUs to memory-constrained edge devices.

All models below are part of the official Hugging Face collection.

🔷 Core Models

1. jdopensource/JoyAI-LLM-Flash
   Full-precision flagship model for high-quality inference and advanced reasoning.
   👉 https://huggingface.co/jdopensource/JoyAI-LLM-Flash

2. jdopensource/JoyAI-LLM-Flash-Base
   Streamlined base version for rapid experimentation and foundational deployment.
   👉 https://huggingface.co/jdopensource/JoyAI-LLM-Flash-Base

🔶 Optimized & Quantized Variants

1. jdopensource/JoyAI-LLM-Flash-FP8
   FP8 quantization — strong balance between performance and efficiency.
   👉 https://huggingface.co/jdopensource/JoyAI-LLM-Flash-FP8

2. jdopensource/JoyAI-LLM-Flash-INT4
   Ultra-compact INT4 version for extremely limited VRAM environments.
   👉 https://huggingface.co/jdopensource/JoyAI-LLM-Flash-INT4

3. jdopensource/JoyAI-LLM-Flash-Block-INT8
   Block-wise INT8 quantization for improved memory efficiency.
   👉 https://huggingface.co/jdopensource/JoyAI-LLM-Flash-Block-INT8

4. jdopensource/JoyAI-LLM-Flash-Channel-INT8
   Channel-wise INT8 optimization for balanced throughput and accuracy.
   👉 https://huggingface.co/jdopensource/JoyAI-LLM-Flash-Channel-INT8

5. jdopensource/JoyAI-LLM-Flash-GGUF
   GGUF format for broad compatibility (e.g., llama.cpp, Ollama, CPU inference).
   👉 https://huggingface.co/jdopensource/JoyAI-LLM-Flash-GGUF

🎯 Deploy Anywhere

Whether you are running:

1. High-throughput GPU servers

2. Consumer-grade GPUs

3. Low-memory edge devices

4. CPU-only local inference

The official JoyAI-LLM-Flash collection provides a tailored option for your hardware stack.

Choose the precision. Match your memory budget. Maximize performance.