💥 Okay, yesterday we had MiniCPM5-2B, but today @OpenSquilla turned Qwen3.5-9B into a ~5.7GB LOCAL agent model, and their training method is interesting. The new NeoHorse-1-9B family is a post-trained Qwen3.5-9B specifically for agents, tool use + coding. It learned from actual multi-model agent execution traces. Setup ... 🔧 tool calls ❌ failed attempts 🔀 model handoffs 🧠 changed plans ✅ environment-verified successes Then they fed those experiences back into the 9B model. Their reported results vs Qwen3.5-9B: 🤖 QwenClaw: 44.04 → 48.73 📌 PinchBench: 74.55 → 82.25 🔄 VitaBench: 31.25 → 42.25 🛠 BFCL v4: 64.88 → 67.43 💻 HumanEval: 92.68 → 98.17 Overall: 65.60 → 69.04 This is the model I'm most interested in for ThumbLLM 🧠 ~9B parameters 💾 Q4_K_M GGUF ~5.7GB 🦙 llama.cpp 🟢 Ollama 🖥️ LM Studio 🤖 Hermes / OpenClaw 📚 262K native context ⚖️ Apache 2.0 So you can run this entirely locally on CPU or GPU. This isn't a bigger model. It's an attempt to make the same small local model better at actually DOING things. 🔥 ⚠️ Benchmarks are reported by the NeoHorse team and I want to do these tests myself next.