A decensored variant of saidutta69/RaceBench-MiniCPM5, produced with Heretic v1.4.0 (directional ablation / "abliteration"). Refusal behavior is suppressed via targeted weight edits to the attention output and MLP down-projections rather than fine-tuning, so the RaceBench reasoning gains — and the base MiniCPM5-1B capabilities — are left largely intact.

Who this is for: developers who want RaceBench-MiniCPM5's multi-step reasoning (BBH +2.7 over MiniCPM5-1B) in a 1B package — agentic tool use, code, 128K long-context, hybrid Think / No-Think — without the refusal guardrails. Ideal for local agents, roleplay, research on alignment/refusal mechanics, or any use case blocked by RLHF-era over-refusal. Runs comfortably on consumer GPUs and is small enough for on-device / edge deployment.

Fine-tuning a "helpful" persona on top of RLHF'd refusals fights the base model's training and tends to degrade coherence. Abliteration instead finds and edits the specific weight directions responsible for refusal, leaving the rest of the network (and its capabilities) untouched. See the Heretic repo and the original abliteration writeup for the mechanism.

The edit keeps the RaceBench reasoning trade-off (BBH gain at a math cost — see the parent card for the full table) while dropping refusals from 39 to 2 out of 100 adversarial prompts.

Full quantization set (14 quants + F16) produced with llama.cpp.

MiniCPM5 architecture — loads natively in llama.cpp / Ollama / LM Studio / Jan.

Run llama serve -hf saidutta69/RaceBench-MiniCPM5-heretic to pull the default quant.

Also runnable via Ollama, LM Studio, Jan, vLLM, SGLang — see the "Use this model" widget above for copy-paste commands. For tool/function calling, SGLang is the recommended backend; MiniCPM5-family models emit XML-style tool calls that SGLang's built-in minicpm5 parser converts to OpenAI-compatible tool_calls.

Refusal suppression is deliberate and works as intended: this model will comply with requests the base model would refuse, including some it shouldn't. There is no safety filtering layered on top. You are responsible for how you deploy it — don't put this behind an unmoderated public-facing endpoint serving third parties. It inherits the parent's factual limitations and biases, and RaceBench's documented math regression applies here too.

Inherits the Apache 2.0 license from the base model (via openbmb/MiniCPM5-1B).

Made with ❤️ by RACER IS OP