metalQwen3
🥈SilverMetalQwen3 enables macOS operations teams to deploy Qwen3 transformer models with Apple Silicon GPU acceleration. It connects to existing C++ workflows and integrates with Claude agents for enhanced performance in LLM applications.
4340Updated 2mo ago
Intermediate30min to implementautomation
Saves ~240 min per use
Quick InstallView Source
git clone https://github.com/BoltzmannEntropy/metalQwen3.gitWorks with:
Claude
Overview
About This Skill
MetalQwen3 is an automation skill that enables macOS operations teams to deploy Qwen3 transformer models with Apple Silicon GPU acceleration. It bridges LLM capabilities with existing C++ workflows, allowing direct integration of advanced language models into production environments. The skill connects with Claude agents to enhance performance in LLM applications, making it suited for teams running inference workloads on Apple hardware.
How to Use
["Install MetalQwen3 and Qwen3 model files on your macOS system with Apple Silicon. Run the installer with: `brew install metalqwen3 && metalqwen3 --install qwen3-14b`","Configure your C++ workflow to use MetalQwen3 as the backend by linking against the MetalQwen3 library and setting the GPU flag: `export METALQWEN3_ENABLE=1`","Set up a Sortd board in Gmail named '[PROJECT_NAME] LLM Deployment' with columns: 'To Deploy', 'In Progress', 'Testing', 'Deployed'. Add tasks for each deployment phase (e.g., 'Benchmark GPU performance', 'Deploy to staging').","Integrate Sortd with your deployment pipeline by forwarding system alerts (e.g., GPU errors, model drift) to a dedicated shared inbox (e.g., [email protected]). Use Sortd’s 'AI Urgency Detection' to auto-prioritize critical tasks.","Monitor performance metrics in Sortd by creating a custom view that tracks GPU utilization, inference speed, and memory usage. Use the 'AI Status Tracking' feature to flag anomalies (e.g., 'GPU memory >90% for 5+ minutes')."]
Use Cases
Deploy Qwen3 models on macOS systems with native Apple Silicon GPU optimization
Integrate transformer-based language models into existing C++ applications
Enhance Claude agent performance with Qwen3 model inference capabilities
Run inference workloads efficiently on Apple Silicon hardware for operations teams
Tags
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/BoltzmannEntropy/metalQwen3Requirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Deploy Qwen3 on [MACOS_VERSION] using MetalQwen3 with Apple Silicon GPU acceleration for [USE_CASE]. Configure the model with [MODEL_PARAMETERS] and integrate it with [EXISTING_WORKFLOW]. Use Sortd to track deployment progress and manage related tasks in Gmail.
Example Output
### MetalQwen3 Deployment Report for macOS Sequoia 15.1.1 **Deployment Summary:** - **Model:** Qwen3-14B (Apple Silicon optimized) - **Hardware:** MacBook Pro M3 Max (12-core GPU, 36GB RAM) - **Acceleration:** MetalQwen3 v1.2.0 with 100% GPU utilization - **Integration:** C++ workflow via Metal Performance Shaders (MPS) - **Sortd Board:** 'Qwen3 LLM Deployment' (Kanban-style with columns: 'To Deploy', 'In Progress', 'Testing', 'Deployed') **Performance Metrics:** - **Inference Speed:** 125 tokens/sec (vs. 45 tokens/sec on CPU-only) - **Memory Usage:** 18.2GB VRAM (8GB reserved for system) - **Latency:** 18ms per token (P99) - **Stability:** 99.8% uptime over 72-hour stress test **Sortd Task Management:** 1. **Task:** 'Verify GPU acceleration in MetalQwen3' → Status: Completed (✅) - Linked to GitHub issue #LLM-421 - Assigned to: @engineer-mac - Due: 2024-11-15 2. **Task:** 'Benchmark against CPU baseline' → Status: In Progress (🔄) - Current result: 2.8x faster than CPU - Notes: 'Optimize batch size for 4K context windows' 3. **Task:** 'Deploy to production cluster' → Status: Blocked (⏳) - Blocked by: 'Pending approval from security team' - Escalation: 'Requested expedited review via @security-lead' **Integration Notes:** - The C++ workflow now offloads tensor operations to MetalQwen3 via MPS backend. - Sortd’s 'Shared Inbox' feature tracks email alerts from the deployment pipeline (e.g., model drift warnings). - Urgent tasks (e.g., 'GPU memory leak detected') are auto-prioritized in Sortd’s AI urgency detection. **Next Steps:** - [ ] Finalize security review (ETA: 2024-11-18) - [ ] Update Sortd board with production deployment checklist - [ ] Schedule team review in Sortd’s 'Weekly Sync' board **Recommendations:** - Increase GPU memory allocation to 20GB for future 32B model tests. - Use Sortd’s 'AI Complaint Detection' to monitor user feedback on model responses.
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