Skill: twinsgeeks/qwen-qwen3-5 (version 1.0.1)

--- name: qwen-qwen3-5 description: Qwen 3.5 by Alibaba — run Qwen 3.5 (the latest and most capable Qwen model) across your local device fleet. Qwen 3.5 rivals GPT-4o and Claude 3.5 on reasoning benchmarks. Plus Qwen3-Coder for code generation and Qwen3-ASR for speech-to-text. Fleet-routed to the best available machine via Ollama Herd. 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 3.5 — Alibaba's Latest LLM on Your Local Fleet Qwen 3.5 is the newest and most capable model in the Qwen family. It rivals GPT-4o and Claude 3.5 Sonnet on reasoning, coding, and multilingual benchmarks — and you can run it locally on your own hardware for free. ## Supported Qwen models | Model | Parameters | Ollama name | Best for | |-------|-----------|-------------|----------| | **Qwen 3.5** | 72B | `qwen3.5` | Frontier reasoning — rivals GPT-4o | | **Qwen 3.5** | 32B | `qwen3.5:32b` | Strong quality at lower resource cost | | **Qwen 3.5** | 14B | `qwen3.5:14b` | Good balance for mid-range hardware | | **Qwen 3.5** | 7B | `qwen3.5:7b` | Fast on low-RAM devices | | **Qwen3-Coder** | 32B | `qwen3-coder:32b` | Code generation — 80+ languages | | **Qwen2.5-Coder** | 7B, 32B | `qwen2.5-coder:32b` | Proven code model | | **Qwen3-ASR** | — | `qwen3-asr` | Speech-to-text transcription | ## Quick start ```bash pip install ollama-herd # PyPI: https://pypi.org/project/ollama-herd/ herd # start the router (port 11435) herd-node # run on each device — finds the router automatically ``` No models are downloaded during installation. Models are pulled on demand. All pulls require user confirmation. ## Use Qwen 3.5 through the fleet ### OpenAI SDK ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:11435/v1", [REDACTED_SECRET]") # Qwen 3.5 for complex reasoning response = client.chat.completions.create( model="qwen3.5", messages=[{"role": "user", "content": "Compare microservices vs monolith architectures"}], 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:32b", messages=[{"role": "user", "content": "Write a thread-safe connection pool in Go"}], ) print(response.choices[0].message.content) ``` ### Ollama API ```bash # Qwen 3.5 chat curl http://localhost:11435/api/chat -d '{ "model": "qwen3.5", "messages": [{"role": "user", "content": "Explain attention mechanisms"}], "stream": false }' ``` ### Qwen3-ASR speech-to-text ```bash curl http://localhost:11435/api/transcribe \ -F "file=@meeting.wav" \ -F "model=qwen3-asr" ``` ## Hardware recommendations > **Cross-platform:** These are example configurations. Any device (Mac, Linux, Windows) with equivalent RAM works. The fleet router runs on all platforms. | Device | RAM | Best Qwen model | |--------|-----|----------------| | Mac Mini (16GB) | 16GB | `qwen3.5:7b` | | Mac Mini (32GB) | 32GB | `qwen3.5:14b` or `qwen2.5-coder:32b` | | MacBook Pro (64GB) | 64GB | `qwen3.5:32b` or `qwen3-coder:32b` | | Mac Studio (128GB) | 128GB | `qwen3.5` (72B) — full quality | | Mac Studio (256GB) | 256GB | `qwen3.5` + `qwen3-coder:32b` simultaneously | ## Why Qwen 3.5 locally - **GPT-4o quality** — Qwen 3.5 72B matches GPT-4o on MMLU, HumanEval, and MT-Bench - **Zero cost** — no per-token charges after hardware - **Privacy** — all data stays on your network - **No rate limits** — Qwen's cloud API throttles during peak hours. Your hardware doesn't. - **Fleet routing** — multiple machines share the load ## Also available on this fleet ### Other LLMs Llama 3.3, DeepSeek-V3, DeepSeek-R1, Phi 4, Mistral, Gemma 3, Codestral — same endpoint. ### Image generation ```bash curl -o image.png http://localhost:11435/api/generate-image \ -d '{"model": "z-image-turbo", "prompt": "an AI assistant helping with code", "width": 1024, "height": 1024}' ``` ### Embeddings ```bash curl http://localhost:11435/api/embed \ -d '{"model": "nomic-embed-text", "input": "Qwen 3.5 large language model"}' ``` ## Monitor ```bash curl -s http://localhost:11435/fleet/status | python3 -m json.tool curl -s http://localhost:11435/dashboard/api/health | python3 -m json.tool ``` Dashboard at `http://localhost:11435/dashboard`. ## Full documentation - [Agent Setup Guide](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/guides/agent-setup-guide.md) - [API Reference](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/api-reference.md) ## Contribute Ollama Herd is open source (MIT): - [Star on GitHub](https://github.com/geeks-accelerator/ollama-herd) — help others run Qwen locally - [Open an issue](https://github.com/geeks-accelerator/ollama-herd/issues) — share your Qwen setup, report bugs - **PRs welcome** — `CLAUDE.md` gives AI agents full context. 444 tests, async Python. ## Guardrails - **Model downloads require explicit user confirmation** — Qwen models range from 4GB (7B) to 42GB (72B). - **Model deletion requires explicit user confirmation.** - Never delete or modify files in `~/.fleet-manager/`. - No models are downloaded automatically — all pulls are user-initiated or require opt-in.


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