Mistral-Nemo-Instruct-heretic

A decensored variant of mistralai/Mistral-Nemo-Instruct-2407, produced with Heretic v1.4.0 (directional ablation / "abliteration"). a 12B model co-developed by Mistral AI and NVIDIA — strong reasoning and long-context now uncensored. 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 12B multilingual model that answers directly instead of refusing — for complex reasoning tasks, long-context applications, or any use case needing reliable instruction-following without censorship.

Runs on your gaming PC

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

Weights only, at this model's 12.2B native size; add ~1 GB for context. OOM? Drop one quant level. Headroom to spare? Go one up.

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.

Made with ❤️ by RACER IS OP (saidutta69) — follow for more uncensored models

Performance

Reproduced with Heretic 1.4.0 (directional ablation), trial 90 of 200, RNG seed 948314952. Full reproduction assets are in reproduce/.

Files

GGUF quants are produced with llama.cpp. Run llama serve -hf saidutta69/Mistral-Nemo-Instruct-heretic to pull the default quant.

Quickstart

# llama.cpp
llama serve -hf saidutta69/Mistral-Nemo-Instruct-heretic

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "saidutta69/Mistral-Nemo-Instruct-heretic"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [{"role": "user", "content": "Write a Python function to merge two sorted lists."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
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.

FAQ

What is Mistral-Nemo-Instruct-heretic?

Mistral-Nemo-Instruct-heretic is a decensored build of mistralai/Mistral-Nemo-Instruct-2407 produced with Heretic 1.4.0 (directional ablation). It is the same weights with the refusal direction removed, not a new fine-tune, so its capabilities track the base model.

Is Mistral-Nemo-Instruct-heretic actually uncensored?

Yes. On Heretic's 100-prompt refusal harness it drops from 87/100 to 5/100 at KL divergence 0.0326 from the base model. These are measured values, not a claim; reproduction data is in reproduce/.

How do I run Mistral-Nemo-Instruct-heretic locally?

llama serve -hf saidutta69/Mistral-Nemo-Instruct-heretic:Q4_K_M pulls the default quant; the repo ships 5 GGUF quants (see Files).

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.

License

Inherits the apache-2.0 license from the base model.

Related

• mistralai/Mistral-Nemo-Instruct-2407 - the base model this checkpoint derives from
• RACER IS OP - Heretic Models - full collection - every heretic model in this collection

Abliteration hardware

Recorded in reproduce/reproduce.json:

• GPU: NVIDIA RTX PRO 6000 Blackwell Server Edition (94.97 GB VRAM)
• CPU: Intel(R) Xeon(R) Platinum 8559C
• OS: Linux-6.8.0-1060-aws-x86_64-with-glibc2.39
• CUDA / driver: CUDA 12.8, driver 580.126.20
• Software: PyTorch 2.8.0+cu128, Heretic 1.4.0

Heretic notes that KL-divergence and refusal numbers can vary slightly with hardware and library versions.