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 MiniCPM5-1B-Claude-Opus-Fable5-Thinking, a 1.08B-parameter model fine-tuned on Claude Fable 5 traces that runs fully on local hardware with a 657MB Q4_K_M GGUF quant. The model, built on OpenBMB's MiniCPM5-1B base, uses supervised fine-tuning on generated outputs rather than weight-level distillation, meaning it imitates response format and style without absorbing frontier reasoning capabilities. No benchmarks or training dataset have been published, leaving capability claims unverifiable.

A community developer, GnLOLot https://huggingface.co/GnLOLot , has published a 1B model that runs fully on local hardware. The model is MiniCPM5-1B-Claude-Opus-Fable5-Thinking https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking , with GGUF builds https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF 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 https://huggingface.co/openbmb/MiniCPM5-1B . That base is a real, documented release from OpenBMB https://github.com/OpenBMB/MiniCPM . It is a dense 1.08B-parameter model using a standard LlamaForCausalLM architecture. It has 24 layers, grouped-query attention, and a 131,072-token context length. OpenBMB reports 1B-class open-source SOTA within its own comparison set. The base already ships a native thinking template. Reasoning is toggled through enable thinking , giving both a Think and a No Think mode. The derivative model keeps that template and MiniCPM5’s tool-call format. On top of that base, the developer applied a fine-tune. The card states the model is ‘further fine-tuned on Fable 5 data’ to improve coding and instruction following. The GGUF card repeats this as ‘post-trained on Fable 5 data.’ How it is actually built The described method is not classical distillation. You do not shrink the original model. Instead you generate many conversations with a teacher model. You capture its replies and reasoning traces as text. You then supervised-fine-tune a smaller base model on those traces. This distinction is important for accuracy. Classical distillation transfers signal from a teacher’s logits or weights. No one has access to Claude’s weights or logits. So this is supervised fine-tuning on generated outputs, not weight-level distillation. OpenBMB’s own base model, by contrast, uses a documented On-Policy Distillation https://thinkingmachines.ai/blog/on-policy-distillation/ stage between its own teacher and student checkpoints. The practical effect is that the 1B model learns to imitate response format and style. It does not absorb the teacher’s underlying capability. A 1B parameter budget cannot hold frontier-scale reasoning. The specs that check out The context window is 128K tokens, inherited from the base config.json 131,072 . The GGUF repository ships four quantizations. Q4 K M is roughly 657MB and is labeled the smallest footprint. Q5 K M is roughly 751MB. Q8 0 is roughly 1.1GB and is the maintainer’s recommended default. F16 is roughly 2.1GB. The ‘657MB footprint’ is the smallest quant, not the default build. The model loads directly in llama.cpp, Ollama https://ollama.com/ , LM Studio, jan, and KoboldCpp. Interactive: how the build works The explainer below walks the build pipeline, the footprint tradeoffs, and the honest split of what a fine-tune can and cannot carry over. How to run it The GGUF card gives a one-line path through Ollama: ollama run hf.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF:Q4 K M The same repository documents llama.cpp, LM Studio, jan, and KoboldCpp. Recommended sampling for Think mode is temperature=0.9, top p=0.95 . The model may emit reasoning blocks before the final answer, which downstream apps can strip. Key Takeaways - The model is a supervised fine-tune of OpenBMB’s MiniCPM5-1B on Claude Fable 5 traces, not a weight-level distillation. - Real specs: 128K context, GGUF quants from ~657MB Q4 K M to ~2.1GB F16 , Q8 0 the recommended default. - Fine-tuning on outputs transfers format and style, not frontier reasoning or broad knowledge. - No benchmarks or training dataset are published, so capability claims are currently unverifiable. - Apache-2.0 covers the base weights only; training on Claude outputs raises a licensing question the card leaves open. Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.