RaceBench-MiniCPM5-heretic

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.

Why abliteration instead of fine-tuning

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.

Runs on your gaming PC

Full GGUF ladder included — pick the quant that fits your card:

Weights only, at this model's ~1.1B native size; add ~1 GB for context. OOM? Drop one quant level. Headroom to spare? Go one up.

Abliteration parameters

Performance

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.

Made with ❤️ by RACER IS OP — follow for more uncensored models

Files

GGUF quantizations

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.

Quickstart

# llama.cpp
llama serve -hf saidutta69/RaceBench-MiniCPM5-heretic

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "saidutta69/RaceBench-MiniCPM5-heretic"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [{"role": "user", "content": "Who are you? Please briefly introduce yourself."}]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    enable_thinking=False,   # set True for Think mode
    return_dict=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

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.

Responsible use

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.

License

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

Related

• saidutta69/RaceBench-MiniCPM — the parent model
• openbmb/MiniCPM5-1B — the base model
• saidutta69/MiniCPM5-1B-heretic
• saidutta69/Qwen3.5-2B-heretic

Made with ❤️ by RACER IS OP