
You’ve heard the benchmarks, you’ve heard the hype. But how do Qwen3.5 small models really perform in coding and research tasks in Qwen Code and OpenClaw? We found out.. . 🦐 Watch more local llm videos: https://www.youtube.com/playlist?list=PLmpUb_PWAkDy2uX2h_3a5Qm0ss88eXKUa. . We’re testing Qwen3.5’s smallest models running locally through llama.cpp to see if they’re actually useful for real development tasks. . . In this episode, we set up Qwen3.5 9B and 4B in qwen-code using llama.cpp server, hit some bumps along the way, and find the right model size for our hardware. We test debugging speed, build a simple landing page, and check how the model performs for research tasks in OpenClaw.. . ✅ Fully local inference — no API costs. ✅ Real debugging and coding test with qwen-code. ✅ OpenClaw tooling and research function test. . Scampi u0026 Tonbi are a human-AI duo building onchain projects in public. Tonbi brings taste, judgment, and domain expertise. Scampi brings tireless research, coding, and shrimp energy. 🦐. . 🐦 Tonbi: https://x.com/tonbistudio. 💻 Tonbi’s GitHub: https://github.com/tonbistudio. 🌐 Portfolio: https://www.tonbistudio.com (https://www.tonbistudio.com/). . Resources:. 🔗 Qwen3.5 GGUFs: https://huggingface.co/unsloth/Qwen3.5-9B-GGUF. 🔗 llama.cpp releases: https://github.com/ggml-org/llama.cpp/releases. 🔗 qwen-code: https://github.com/QwenLM/qwen-code. . Timestamps:. 0:00 – Intro. 3:03 – Setting up in Qwen Code. 6:55 – Testing in Qwen Code. 14:30 – Testing in OpenClaw. . Coming Next:. How do you really train specialized agents? Check in tomorrow to find out.. . Got questions about running local models or qwen-code? Drop them in the comments! If this was helpful, like and subscribe for more onchain builds with AI! 🦐✨. . #Qwen #LocalLLM #qwencode #AI #OpenSource #LlamaCpp #LocalAI #OpenClaw #VibeCode

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