Skill: twinsgeeks/qwen-qwen3 (version 1.0.2)

--- name: qwen-qwen3 description: Qwen Qwen3 — run Qwen3.5, Qwen3, Qwen3-Coder, Qwen2.5-Coder, and Qwen3-ASR across your local fleet. LLM inference, code generation, and speech-to-text from Alibaba's Qwen family via Ollama Herd. Cross-platform (macOS, Linux, Windows). Zero cloud costs. version: 1.0.1 homepage: https://github.com/geeks-accelerator/ollama-herd metadata: {"openclaw":{"emoji":"sparkles","requires":{"anyBins":["curl","wget"],"optionalBins":["python3","pip"]},"configPaths":["~/.fleet-manager/latency.db","~/.fleet-manager/logs/herd.jsonl"],"os":["darwin","linux","windows"]}} --- # Qwen — Run Qwen Models Across Your Local Fleet Run Qwen3.5, Qwen3, Qwen3-Coder, and Qwen ASR on your own hardware. The fleet router picks the best device for every request — chat, code generation, and speech-to-text from one endpoint. ## Supported Qwen models ### LLM (Chat & Reasoning) | Model | Parameters | Ollama name | Best for | |-------|-----------|-------------|----------| | **Qwen3.5** | 0.8B–397B MoE | `qwen3.5` | Latest — multimodal, best reasoning | | **Qwen3** | 0.6B–235B MoE | `qwen3` | Competitive with GPT-4o | | **Qwen2.5** | 0.5B–72B | `qwen2.5` | Proven, stable, multilingual | ### Code Generation | Model | Parameters | Ollama name | Best for | |-------|-----------|-------------|----------| | **Qwen3-Coder** | 30B MoE (3.3B active) | `qwen3-coder` | Agentic coding workflows | | **Qwen2.5-Coder** | 0.5B–32B | `qwen2.5-coder` | Code — matches GPT-4o at 32B | ### Speech-to-Text | Model | Parameters | Tool | Best for | |-------|-----------|------|----------| | **Qwen3-ASR** | 0.6B–1.7B | `mlx-qwen3-asr` | State-of-the-art local transcription | ## Setup ```bash pip install ollama-herd herd # start the router (port 11435) herd-node # run on each machine # Pull Qwen models ollama pull qwen3.5:32b ollama pull qwen3-coder ``` For speech-to-text: ```bash uv tool install "mlx-qwen3-asr[serve]" --python 3.14 curl -X POST http://localhost:11435/dashboard/api/settings \ -H "Content-Type: application/json" -d '{"transcription": true}' ``` Package: [ollama-herd](https://pypi.org/project/ollama-herd/) | Repo: [github.com/geeks-accelerator/ollama-herd](https://github.com/geeks-accelerator/ollama-herd) ## Use Qwen through the fleet ### OpenAI SDK ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:11435/v1", [REDACTED_SECRET]") # Qwen3.5 for general chat response = client.chat.completions.create( model="qwen3.5:32b", messages=[{"role": "user", "content": "Hello"}], stream=True, ) for chunk in response: print(chunk.choices[0].delta.content or "", end="") ``` ### Qwen3-Coder for code ```python response = client.chat.completions.create( model="qwen3-coder", messages=[{"role": "user", "content": "Write a FastAPI CRUD app with SQLAlchemy"}], ) print(response.choices[0].message.content) ``` ### Qwen ASR for transcription ```bash curl http://localhost:11435/api/transcribe -F "audio=@meeting.wav" ``` ```python import httpx def transcribe(audio_path): with open(audio_path, "rb") as f: resp = httpx.post( "http://localhost:11435/api/transcribe", files={"audio": (audio_path, f)}, timeout=300.0, ) resp.raise_for_status() return resp.json()["text"] ``` ### Ollama API ```bash # Qwen3.5 chat curl http://localhost:11435/api/chat -d '{ "model": "qwen3.5:32b", "messages": [{"role": "user", "content": "Explain transformers"}], "stream": false }' # Qwen2.5-Coder curl http://localhost:11435/api/chat -d '{ "model": "qwen2.5-coder:32b", "messages": [{"role": "user", "content": "Optimize this SQL query: ..."}], "stream": false }' ``` ## Hardware recommendations > **Cross-platform:** These are example configurations. Any device (Mac, Linux, Windows) with equivalent RAM works. The fleet router runs on all platforms. | Model | Min RAM | Recommended hardware | |-------|---------|---------------------| | `qwen3.5:0.8b` | 2GB | Any Mac | | `qwen3.5:9b` | 8GB | Mac Mini M4 (16GB) | | `qwen3.5:32b` | 24GB | Mac Mini M4 Pro (48GB) | | `qwen3.5:122b-a10b` | 64GB | Mac Studio M4 Max (128GB) | | `qwen3.5:397b-a17b` | 256GB+ | Mac Studio M3 Ultra (512GB) | | `qwen3-coder` | 24GB | Mac Mini M4 Pro (48GB) | | `qwen2.5-coder:32b` | 24GB | Mac Mini M4 Pro (48GB) | | Qwen3-ASR (0.6B) | 1.2GB | Any Mac | | Qwen3-ASR (1.7B) | 3.4GB | Any Mac (8GB+) | ## Why run Qwen locally - **Zero cost** — no per-token charges for Qwen API - **Privacy** — Chinese and English content stays on your devices - **Full Qwen family** — chat, code, reasoning, and speech-to-text from one fleet - **No rate limits** — Alibaba Cloud throttles API access. Local runs unlimited - **Fleet routing** — multiple machines share the load. The router picks the fastest available ## The Qwen advantage on this fleet Qwen models are uniquely suited for fleet routing: - **MoE architecture** — Qwen3.5 (397B total, 17B active) and Qwen3-Coder (30B total, 3.3B active) use Mixture of Experts. Only a fraction of parameters activate per request, making them fast despite large total size. - **Size variety** — from 0.6B to 397B, there's a Qwen model for every device in your fleet. Small Macs run the small models, big Macs run the big ones. - **Code + Chat + STT** — Qwen covers three modalities. One vendor, one fleet, three capabilities. ## Also available on this fleet ### Other LLM models Llama 3.3, DeepSeek-V3, DeepSeek-R1, Phi 4, Mistral, Gemma 3 — any Ollama model routes through the same endpoint. ### Image generation ```bash curl -o image.png http://localhost:11435/api/generate-image \ -H "Content-Type: application/json" \ -d '{"model":"z-image-turbo","prompt":"a sunset","width":1024,"height":1024,"steps":4}' ``` ### Embeddings ```bash curl http://localhost:11435/api/embeddings -d '{"model":"nomic-embed-text","prompt":"query"}' ``` ## Dashboard `http://localhost:11435/dashboard` — monitor Qwen requests alongside all other models. Per-model latency, token throughput, error rates, health checks. ## Full documentation [Agent Setup Guide](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/guides/agent-setup-guide.md) ## Guardrails - Never pull or delete Qwen models without user confirmation. - Never delete or modify files in `~/.fleet-manager/`. - If a Qwen model is too large for available memory, suggest a smaller variant or MoE version.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)