Instructions to use 5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use 5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use 5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use 5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default 5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use 5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "5E1Labs/LongShuV3-Qwen3.5-122B-A10B-GameDev-MLX-4bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

LongShu · Architect-V3

A next-generation multi-agent collaboration core brain for game development, based on Qwen3.5-122B-A10B-MoE architecture with deep optimization, running smoothly on consumer-grade hardware.

🌐 Official Website: www.5e1.com — Experience the latest V3 version online

🎮 Model Positioning

Traditional general-purpose LLMs often suffer from severe "hallucinations" or provide superficial suggestions when facing complex system architectures, deep engine APIs (Unreal/Unity), and state machine logic in real game development scenarios.

LongShu V3 builds upon the solid foundation of V2.2, achieving a leap from 97B to 99B parameters through architectural optimization and expanded expert coverage. We've performed deeper domain-specific fine-tuning to create the next-generation core brain of the Multi-Agent System — the Commander Model (TIANCE).

It doesn't just understand code; it understands game engineering. It's no longer a chatbot — it's a true "Technical Director + Lead Architect" .

🏗️ REAP Ecosystem

LongShu V3 is the central brain of an enhanced 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, tech debt identification
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, intelligent retrieval
Coordinator	Hengshu	Team collaboration expert	Intelligent task allocation, cross-functional coordination

⚡ Core Technical Highlights (V3 Upgrades)

Hybrid Attention Architecture V3

60-layer network with 3:1 alternating Linear + Full Attention
Enhanced attention head allocation for better long-range dependency capture
Optimized KV-cache compression, reducing memory footprint by 18% compared to V2.2

Long Context Support (512K)

Extended native support from 256K to 524,288 token context
Can ingest entire project codebases, design docs, and API documentation in one shot
Improved positional encoding for ultra-long sequences

Extreme MoE Sparsity (V3)

105 experts, only 10 activated per token (expanded to 99B total parameters)
New expert routing mechanism with dynamic load balancing
Inference speed of ~42 tokens/s on Mac mini M4 Pro 64GB (17% faster than V2.2)

Game Engine-Aware Hybrid Quantization V3

Core 20 experts: high-precision IQ4_NL/Q5_K
Non-core 85 experts: extreme compression IQ2_XXS
Refined quantization calibration with 99.1% core reasoning capability retention (up from 98.6%)

New: Multi-Modal Scene Understanding

Support for analyzing game screenshots and UI mockups
Can interpret level layouts, HUD designs, and visual references
Integrated vision encoder for scene-aware code generation

New: Real-Time Code Context Awareness

Enhanced ability to track cross-file dependencies in large projects
Improved understanding of Unreal Engine Blueprint-to-C++ translation
Better support for Unity ECS/DOTS architecture patterns

🎯 Specialized Training Data

Data Type	Scale	Description
Real Game Projects	68+	MMO, FPS, ARPG, Roguelike, Sandbox, Survival genres
Core Source Code	2.8B+ Tokens	UE5 (C++), Unity 6 (C#), Godot 4, Lua hot-reload frameworks
Engineering Docs	420K+ Pages	GDDs, system breakdowns, game design logic, performance analysis
High-Quality Online Data	13.5B+	StackOverflow gamedev, GitHub Issues, graphics papers, shader libraries
New: QA/Testing Data	850K+	Automated test cases, bug reproduction steps, regression analysis

💻 Hardware Requirements

Configuration	Recommendation
Mac	M2/M3/M4 series, 64GB Unified Memory
PC	Dual RTX 3090/4090 (24GB)
Format	MLX 4-bit quantized (optimized for Apple Silicon)
Speed	~42 tokens/s (Mac mini M4 Pro)

🚀 Use Cases

Automated Test Case Generation — Auto-generate test plans based on code logic and game design specs
Daily Build Error Diagnosis — Analyze compile/runtime errors with fix suggestions and root cause analysis
Level Toolchain Dispatch — Understand design requirements, dispatch executors for level implementation
System Architecture Design — Decompose complex requirements into modular, scalable architectures
Code Review — Review code quality, identify potential issues, suggest performance optimizations
Shader & Rendering Pipeline Optimization — Analyze and optimize graphics code for target platforms
AI Behavior Tree Design — Generate and debug NPC AI logic, patrol routes, and decision trees

🎮 Real-World Application (In Development)

Sakura Dream Sea (樱梦海) — An Eastern Fantasy Open-World MMO Adventure

The Sakura Dream Sea development team is actively collaborating with LongShu V3 as one of the core pilot partners, integrating the enhanced LongShu agent capabilities into their game development pipeline:

Leveraging the Tiance/Commander Model V3 for global task planning with extended 512K context support
Utilizing Xuangou/Architect for Unreal Engine 5 system architecture optimization and code review
Accelerating core gameplay development (combat systems, AI behavior trees) through Moxing/Executor with improved cross-file awareness
Monitoring server performance and anomaly logs with Zhuzhao/Watcher

⚠️ Status: In development — not yet launched. The Sakura Dream Sea project is currently in active development, with LongShu V3 being integrated into the production pipeline. More details will be shared as the project progresses.

📄 License

Apache 2.0 License

LongShu V3 · AI-Powered Partner for Game Development

Experience the latest version at www.5e1.com

Downloads last month: 230

Safetensors

Model size

99B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit