A decensored variant of GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking, produced with Heretic v1.4.0 (directional ablation / "abliteration"). The base model is itself a V2 fine-tune of openbmb/MiniCPM5-1B on Fable 5 traces, focused on tool/function calling, coding, and instruction-following. Refusal behavior is suppressed via targeted weight edits to the attention output and MLP down-projections rather than fine-tuning, so the base model's knowledge and capabilities are left largely intact.
Who this is for: developers who want a tiny (1B) Thinking model with strong tool-calling and coding ability that answers directly instead of refusing — 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, while keeping MiniCPM5's 128K context and native Think / No-Think chat modes.
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This model is reproducible!
See the README in the reproduce directory for the exact config, full parameter/metric dump, evaluation transcripts, and SHA256 checksums.
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.
KL divergence of 0.0232 is very low — the edit is narrow and targeted rather than a broad perturbation. Refusals dropped from 93 to 3 out of 100 adversarial prompts while preserving the base model's tool-calling, coding, and thinking abilities.
Made with ❤️ by RACER IS OP — follow for more uncensored models
GGUF quants are produced with llama.cpp (MiniCPM5 uses the standard LlamaForCausalLM architecture, so it loads in llama.cpp / Ollama / LM Studio / Jan directly). Run llama serve -hf saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic to pull the default quant.
1# transformers2from transformers import AutoModelForCausalLM, AutoTokenizer
34model_id ="saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic"5tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)6model = AutoModelForCausalLM.from_pretrained(7 model_id,8 trust_remote_code=True,9 torch_dtype="auto",10 device_map="auto",11)1213messages =[{"role":"user","content":"Write a Python function to merge two sorted lists."}]14text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)15inputs = tokenizer(text, return_tensors="pt").to(model.device)16outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)17print(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; this model emits 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 this fine-tune's (and MiniCPM5-1B's) factual limitations and biases; abliteration removes refusal directions, it doesn't add capability or judgment.
License
Inherits the Apache 2.0 license from the base model.
MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking is a compact 1B Thinking language model built on openbmb/MiniCPM5-1B. Compared with V1, this V2 release is further fine-tuned on Fable 5 data with a stronger focus on tool calling / function calling, while also improving coding and instruction-following. It keeps MiniCPM5's native Thinking chat template and XML tool-call format.
