Someone Fine-Tuned OpenBMB’s MiniCPM5-1B on Claude Fable 5 Traces to Ship a 657MB Local Thinking Model
A community developer, GnLOLot, has published a 1B model that runs fully on local hardware. The model is MiniCPM5-1B-Claude-Opus-Fable5-Thinking, with GGUF builds for llama.cpp-compatible runtimes. It needs no API key and makes no cloud calls. The Proposed Model The model is built on openbmb/MiniCPM5-1B. That base is a real, documented release from OpenBMB. It is a dense 1.08B-parameter model using a standard LlamaForCausalLM architecture. It has 24 layers, grouped-query attention, and a
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A developer created a small language model by fine-tuning an existing 1-billion-parameter base model on text traces generated from a larger teacher model, resulting in a model that runs locally at roughly 657MB in size. This approach differs from classical distillation because it trains on generated text outputs rather than transferring weights or internal signals from the teacher model. The practical limitation is that while the smaller model learns to imitate response format and style, it cannot absorb the underlying reasoning capabilities of the larger teacher model due to its limited parameter budget. The model runs on local hardware without requiring API keys or cloud calls, and can be used through various open-source frameworks like llama.cpp and Ollama.
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