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saidutta69/Qwen2.5-0.5B-Instruct-heretic

sourceHugging Faceotherupdated 14d agoView on Hugging Face

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Model Card

Qwen2.5-0.5B-Instruct-heretic

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A decensored variant of Qwen/Qwen2.5-0.5B-Instruct, produced with Heretic (directional ablation / "abliteration"). Refusal behavior is suppressed via targeted weight edits rather than fine-tuning, so the base model's knowledge and instruction-following are left largely intact.

Who this is for: the smallest model in this heretic series — for CPU-only inference, edge/embedded deployment, or anywhere the 3B/14B variants are too heavy. At 0.5B parameters, capability ceiling is inherently lower than the larger siblings regardless of abliteration; use this where footprint matters more than reasoning depth.

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

Runs on your gaming PC

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

Your GPU	Recommended quant	Weights
RTX 3060 / 4070 / 5070 (12 GB)	Q8_0	0.53 GB
RTX 4060 / 3070 (8 GB)	Q6_K	0.51 GB
GTX 1660 Super / 2060 / 3050 laptop (6 GB)	Q5KM	0.42 GB
CPU-only / Apple Silicon	Q4KM	0.40 GB, fits in system RAM

Weights only, at this model's ~0.5B native size; add ~1 GB for context. OOM? Drop one quant level. Headroom to spare? Go one up.

Files

File	Format	Size
`model.safetensors`	BF16/FP16	988 MB
`Qwen2.5-0.5B-Instruct-heretic.gguf`	GGUF, F16 (unquantized)	994 MB
`Qwen2.5-0.5B-Instruct-heretic-Q8_0.gguf`	GGUF, Q8_0	531 MB
`Qwen2.5-0.5B-Instruct-heretic-Q5_K_M.gguf`	GGUF, Q5KM	420 MB
`Qwen2.5-0.5B-Instruct-heretic-Q4_K_M.gguf`	GGUF, Q4KM	398 MB

Quickstart

bash

# llama.cpp
llama serve -hf saidutta69/Qwen2.5-0.5B-Instruct-heretic

python

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "saidutta69/Qwen2.5-0.5B-Instruct-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.

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. At 0.5B parameters, factual reliability is already limited before any abliteration; don't treat compliance as a proxy for correctness.

License