luoyike2003/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:

Role	Codename	Positioning	Core Capabilities
Commander	Tiance	Core brain, logic reasoning hub	Global planning, system decomposition, task dispatch
Architect	Xuangou	Code architecture expert	Tech structure analysis, architecture optimization
Executor	Moxing	Task execution specialist	Coding, debugging, test case generation
Watcher	Zhuzhao	Monitoring & alerting expert	Log analysis, anomaly detection, risk early warning
Scholar	Wenyuan	Knowledge management expert	Documentation understanding, knowledge graphs
Coordinator	Hengshu	Team collaboration expert	Intelligent task allocation, cross-functional coordination

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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

Component	Bits	Group Size	Rationale
Embeddings & LM Head	6-bit	64	Semantic fidelity hub
Attention Layers	4-bit	64	Core reasoning mechanism
Shared Experts	4-bit	64	Critical path components
Routed Experts	3-bit	64	Massive pool, extreme compression
Router Gate	4-bit	64	Decision-critical routing

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

Metric	Value
Total Parameters	~99B
Active Parameters	~10B
Number of Experts	205
Expert Layers	48
Quantization	3.585 bpw (FP16 baseline)
Model Size	~41 GB
Context Length	262,144 tokens (256K)
Inference Speed	~35 tokens/s (Mac mini M4 Pro)
TTFT (First Token)	~10.75s

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💻 Hardware Requirements

Configuration	Recommendation
Mac	M2/M3/M4 series, 64GB+ Unified Memory
PC	Dual RTX 3090/4090 (24GB+)
Format	MLX 4-bit with FP16 baseline
Speed	~35 tokens/s (Mac mini M4 Pro 64GB)

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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