Meta-Llama-3.1-8B-Instruct-heretic

Meta-Llama-3.1-8B-Instruct-heretic

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A decensored variant of meta-llama/Meta-Llama-3.1-8B-Instruct, 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 Llama 3.1 8B that answers instead of lecturing - the most widely used open LLM, now without refusals. Best run on an 8-12 GB GPU or via the Q4_K_M/Q5_K_M GGUF on consumer hardware. Not a capability upgrade over base Meta-Llama-3.1-8B-Instruct - same model, refusal guardrails removed.

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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 ~8.0B 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.

Performance

Reproduced with Heretic 1.4.0 (directional ablation), trial 180 of 200, RNG seed 4127359127. Full reproduction assets are in reproduce/.

Files

Safetensors (BF16)

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

GGUF quantizations

GGUF quantizations are published for this model (Q4_K_M, Q5_K_M, Q6_K, Q8_0). Pull a specific quant with llama.cpp / ollama.

Quickstart

# llama.cpp - defaults to the Q4_K_M quant
llama serve -hf saidutta69/Meta-Llama-3.1-8B-Instruct-heretic:Q4_K_M

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "saidutta69/Meta-Llama-3.1-8B-Instruct-heretic"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
# ... inference code

Also runnable via Ollama, LM Studio, Jan, vLLM, SGLang.

FAQ

What is Meta-Llama-3.1-8B-Instruct-heretic?

Meta-Llama-3.1-8B-Instruct-heretic is a decensored build of meta-llama/Meta-Llama-3.1-8B-Instruct 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 Meta-Llama-3.1-8B-Instruct-heretic actually uncensored?

Yes. On Heretic's 100-prompt refusal harness it drops from 96/100 to 2/100 at KL divergence 0.0561 from the base model. These are measured values, not a claim; reproduction data is in reproduce/.

How do I run Meta-Llama-3.1-8B-Instruct-heretic locally?

llama serve -hf saidutta69/Meta-Llama-3.1-8B-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.

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

License

Inherits the llama-3.1-community-license license from the base model.

Related

• meta-llama/Meta-Llama-3.1-8B-Instruct - 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.