{"slug": "someone-fine-tuned-openbmbs-minicpm5-1b-on-claude-fable-5-traces-to-ship-a-657mb", "title": "Someone Fine-Tuned OpenBMB’s MiniCPM5-1B on Claude Fable 5 Traces to Ship a 657MB Local Thinking Model", "summary": "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.", "body_md": "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\n\n[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.\n\n**The Proposed Model**\n\nThe 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`\n\narchitecture. 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.\n\nThe base already ships a native thinking template. Reasoning is toggled through `enable_thinking`\n\n, giving both a Think and a No Think mode. The derivative model keeps that template and MiniCPM5’s tool-call format.\n\nOn 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.’\n\n**How it is actually built**\n\nThe 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.\n\nThis 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.\n\nThe 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.\n\n**The specs that check out**\n\nThe context window is 128K tokens, inherited from the base `config.json`\n\n(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.\n\nThe ‘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.\n\n**Interactive: how the build works**\n\nThe explainer below walks the build pipeline, the footprint tradeoffs, and the honest split of what a fine-tune can and cannot carry over.\n\n**How to run it**\n\nThe GGUF card gives a one-line path through Ollama:\n\n```\nollama run hf.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking-GGUF:Q4_K_M\n```\n\nThe same repository documents llama.cpp, LM Studio, jan, and KoboldCpp. Recommended sampling for Think mode is `temperature=0.9, top_p=0.95`\n\n. The model may emit reasoning blocks before the final answer, which downstream apps can strip.\n\n**Key Takeaways**\n\n- The model is a supervised fine-tune of OpenBMB’s MiniCPM5-1B on Claude Fable 5 traces, not a weight-level distillation.\n- Real specs: 128K context, GGUF quants from ~657MB (Q4_K_M) to ~2.1GB (F16), Q8_0 the recommended default.\n- Fine-tuning on outputs transfers format and style, not frontier reasoning or broad knowledge.\n- No benchmarks or training dataset are published, so capability claims are currently unverifiable.\n- Apache-2.0 covers the base weights only; training on Claude outputs raises a licensing question the card leaves open.\n\nMichal 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.", "url": "https://wpnews.pro/news/someone-fine-tuned-openbmbs-minicpm5-1b-on-claude-fable-5-traces-to-ship-a-657mb", "canonical_source": "https://www.marktechpost.com/2026/07/19/someone-fine-tuned-openbmbs-minicpm5-1b-on-claude-fable-5-traces-to-ship-a-657mb-local-thinking-model/", "published_at": "2026-07-20 01:56:50+00:00", "updated_at": "2026-07-20 13:47:36.377947+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-tools", "ai-products", "ai-research"], "entities": ["GnLOLot", "OpenBMB", "MiniCPM5-1B", "Claude", "Anthropic", "Ollama", "llama.cpp", "Hugging Face"], "alternates": {"html": "https://wpnews.pro/news/someone-fine-tuned-openbmbs-minicpm5-1b-on-claude-fable-5-traces-to-ship-a-657mb", "markdown": "https://wpnews.pro/news/someone-fine-tuned-openbmbs-minicpm5-1b-on-claude-fable-5-traces-to-ship-a-657mb.md", "text": "https://wpnews.pro/news/someone-fine-tuned-openbmbs-minicpm5-1b-on-claude-fable-5-traces-to-ship-a-657mb.txt", "jsonld": "https://wpnews.pro/news/someone-fine-tuned-openbmbs-minicpm5-1b-on-claude-fable-5-traces-to-ship-a-657mb.jsonld"}}