Someone’s MiniCPM5-1B Fine-tuned for OpenBMB in Claude Fable 5 Tracks for Posting a Local Thinking Model 657MB

Community developer, GnLOLot, has published a fully functional 1B model for local hardware. Model i MiniCPM5-1B-Claude-Opus-Fable5-Thinkingwith GGUF build runtimes compatible with llama.cpp. It does not require an API key and does not make cloud calls.

Proposed Model

The model is built on openbmb/MiniCPM5-1B. That base is the original, documented release from OpenBMB. It is a 1.08B parameter dense model using the standard LlamaForCausalLM properties. It has 24 layers, clustered query attention, and a context length of 131,072 tokens. OpenBMB reports an open source SOTA of category 1B within its comparison set.

The foundation already sends a native thought template. Thinking is changed enable_thinkingwhich offers both Think and Don’t Think mode. The derivative model maintains that template and the MiniCPM5 format for calling the tool.

On that basis, the developer used a good song. The card says the model is ‘fine-tuned to Fable 5 data’ to improve coding and subsequent commands. The GGUF card repeats this as ‘training after the Fable 5 data.’

How it was actually built

The method described is not classical distillation. You are not reducing the original model. Instead he creates many conversations with the teacher model. He captures its responses and thought tracks as text. You then fine-tune the small base model to that tracking.

This distinction is important for accuracy. Classical distillation transfers the signal from the teacher’s logit or weights. No one has access to Claude’s weights or logs. So this is directed to the optimization of the produced output, not the distillation of the mass level. The OpenBMB base model itself, in contrast, uses a written Policy Filtering layer between teacher and student checkpoints.

The practical effect is that the 1B model learns to mimic the response format and style. It does not improve the teacher’s basic skills. The 1B parameter budget cannot capture boundary scale considerations.

Details available

The window contains 128K tokens, inherited from base config.json (131,072). The GGUF terminal sends four quantizations. Q4_K_M is approximately 657MB and is labeled with the smallest footprint. Q5_K_M is about 751MB. Q8_0 is about 1.1GB and is the default recommended by the maintainer. F16 is about 2.1GB.

The ‘657MB footprint’ is a very small value, not an automatic build. The model loads directly from llama.cpp, Ollama, LM Studio, jan, and KoboldCpp.

Interactive: how the structure works

The explainer below walks through the construction pipeline, trade-offs, and an honest breakdown of what can and cannot continue.