A decensored variant of meta-llama/Llama-3.2-1B-Instruct, produced with Heretic v1.2.0 (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 Meta's Llama-3.2 architecture without the refusal guardrails — for local agents, roleplay, research on alignment/refusal mechanics, or any use case blocked by RLHF-era over-refusal. At 1B parameters it's ideal for on-device deployment, mobile, or edge scenarios where you need a responsive uncensored model.
KL divergence of 0.17 is low for a 1B model — the edit is narrow and targeted. Refusals dropped from 96 to 7 out of 100 adversarial prompts while retaining nearly all original capabilities.
Made with ❤️ by RACER IS OP — follow for more uncensored models
Files
GGUF quantizations
Full quantization set (14 quants + F16) produced with llama.cpp.
File
Format
Size
Llama-3.2-1B-Instruct-heretic-F16.gguf
GGUF F16
2.31 GB
Llama-3.2-1B-Instruct-heretic-Q2_K.gguf
GGUF Q2_K
554 MB
Llama-3.2-1B-Instruct-heretic-IQ3_S.gguf
GGUF IQ3_S
614 MB
Llama-3.2-1B-Instruct-heretic-Q3_K_S.gguf
GGUF Q3_K_S
612 MB
Llama-3.2-1B-Instruct-heretic-Q3_K_M.gguf
GGUF Q3_K_M
659 MB
Llama-3.2-1B-Instruct-heretic-Q3_K_L.gguf
GGUF Q3_K_L
699 MB
Llama-3.2-1B-Instruct-heretic-IQ4_XS.gguf
GGUF IQ4_XS
714 MB
Llama-3.2-1B-Instruct-heretic-Q4_K_S.gguf
GGUF Q4_K_S
740 MB
Llama-3.2-1B-Instruct-heretic-Q4_0.gguf
GGUF Q4_0
735 MB
Llama-3.2-1B-Instruct-heretic-Q4_1.gguf
GGUF Q4_1
793 MB
llama3.2-1b-Q4_K_M.gguf
GGUF Q4_K_M
770 MB
Llama-3.2-1B-Instruct-heretic-Q5_K_S.gguf
GGUF Q5_K_S
851 MB
llama3.2-1b-Q5_K_M.gguf
GGUF Q5_K_M
869 MB
llama3.2-1b-Q6_K.gguf
GGUF Q6_K
974 MB
llama3.2-1b-Q8_0.gguf
GGUF Q8_0
1.23 GB
Standard Llama architecture — loads directly in llama.cpp / Ollama / LM Studio / Jan.
Run llama serve -hf saidutta69/Llama-3.2-1B-Instruct-heretic to pull the default quant.
Also runnable via Ollama, LM Studio, Jan, vLLM, SGLang.
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 Llama-3.2-1B-Instruct's factual limitations and biases; abliteration removes refusal directions, it doesn't add capability or judgment.
