LongShu-Qwen3.5-99B-Reap-GameDev-MLX-4bit-FP16

LongShu · Reaper-V1 (99B FP16 Edition)

An ultra-large multimodal MoE model with 99B total parameters / 10B active, featuring 205 experts and native vision support. Quantized with FP16 baseline for maximum precision on consumer-grade Apple Silicon hardware.

github: https://github.com/luoyike2003ls/LongShuGameDev

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🎯 Model Overview

This is the FP16 baseline variant of the LongShu Reaper series, built on the massive Qwen3.5-99B MoE architecture with 205 routed experts. Unlike the standard 122B-A10B version, this model features:

• Significantly larger expert pool (205 vs 105 experts) for richer knowledge capacity
• FP16 quantization baseline (not BF16) for better compatibility and precision
• Native multimodal support with vision config injected for image understanding
• 3.585 bits-per-weight extreme compression retaining maximum reasoning capability

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🏗️ REAP Ecosystem

LongShu is the central brain of a complete game development agent network:

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⚡ Core Technical Highlights

Massive MoE Architecture

• 48-layer deep network with hybrid Linear + Full Attention
• 205 routed experts, only a subset activated per token
• Enormous knowledge capacity with efficient sparse computation

FP16 Precision Baseline

• Quantization coefficients stored in float16 (not bfloat16)
• Better compatibility across platforms and inference engines
• Embeddings protected at 6-bit for maximum semantic fidelity

Game Engine-Aware Hybrid Quantization Strategy

Vision Multimodal Support

• Vision config injected from original model
• Supports image_token_id, video_token_id, vision_start/end_token_id
• Ready for visual understanding tasks in game development

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📊 Model Specifications

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💻 Hardware Requirements

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🚀 Quick Start

from mlx_lm import load, generate

model_path = "luoyike2003/LongShu-Qwen3.5-99B-Reap-GameDev-MLX-4bit-FP16"
model, tokenizer = load(model_path)

prompt = "Explain the architecture of a scalable MMO server system"
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

response = generate(model, tokenizer, prompt=text, max_tokens=2048)
print(response)

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🎮 Use Cases

• Game System Architecture Design — Decompose complex requirements into modular architectures
• Unreal/Unity Engine Expert — Deep understanding of engine internals and APIs
• AI Behavior Tree Design — Design and optimize game AI systems
• Code Review & Optimization — Review code quality, identify performance bottlenecks
• Multimodal Game Asset Analysis — Analyze game screenshots, UI designs, and visual assets

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📄 License

Apache 2.0 License

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LongShu · AI-Powered Partner for Game Development

<!-- Updated: 2026-06-19 --> <!-- model: 99B-REAP-FP16 -->