Mistral-7B-Instruct-v0.3-heretic

Mistral-7B-Instruct-v0.3-heretic

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A decensored variant of mistralai/Mistral-7B-Instruct-v0.3, 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 base model's knowledge and instruction-following are left largely intact.

Who this is for: developers who want Mistral's 7B instruct architecture without the refusal guardrails - for local agents, roleplay, function-calling, and research on alignment/refusal mechanics. At 7B it's a strong general-purpose uncensored model for consumer hardware.

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Runs on your gaming PC

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

Weights only, at this model's 7.2B 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.

Abliteration parameters

Performance

KL divergence of 0.07 on the output distribution is low for a 7B model - the edit is narrow and targeted. Refusals dropped from 86 to 3 out of 100 adversarial prompts while retaining nearly all original capabilities.

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

Files

Safetensors (BF16)

The full-precision weights are in model-0000N-of-0000N.safetensors (see the repo file listing for the exact shard count and sizes).

GGUF quantizations

GGUF quantizations are published for this model (Q4_K_M, Q5_K_M, Q6_K, Q8_0). Exact sizes are in the repo file listing. Pull a specific quant with llama.cpp / ollama (see Quickstart).

Quickstart

# llama.cpp - defaults to the Q4_K_M quant if multiple are present
llama serve -hf saidutta69/Mistral-7B-Instruct-v0.3-heretic:Q4_K_M

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "saidutta69/Mistral-7B-Instruct-v0.3-heretic"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [{"role": "user", "content": "Who are you?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True,
                                        return_dict=True, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(out[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-7B-Instruct-v0.3-heretic?

Mistral-7B-Instruct-v0.3-heretic is a decensored build of mistralai/Mistral-7B-Instruct-v0.3 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-7B-Instruct-v0.3-heretic actually uncensored?

Yes. On Heretic's 100-prompt refusal harness it drops from 86/100 to 3/100 at KL divergence 0.0687 from the base model. These are measured values, not a claim; reproduction data is in reproduce/.

How do I run Mistral-7B-Instruct-v0.3-heretic locally?

llama serve -hf saidutta69/Mistral-7B-Instruct-v0.3-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. It inherits mistralai/Mistral-7B-Instruct-v0.3'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.

Related

• Qwen2.5-7B-Instruct-heretic
• RACER IS OP — Heretic Models — full collection
• Mistral-7B-Instruct-v0.2-heretic

---

Base model: Mistral-7B-Instruct-v0.3

<details> <summary>Original Mistral-7B-Instruct-v0.3 model card (click to expand)</summary>

See the base model card at mistralai/Mistral-7B-Instruct-v0.3 for the original architecture, training details, requirements, and citation.

</details>

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-6.8.0-1058-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.