Qwen2.5-0.5B-Instruct-heretic
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) | Q5_K_M | 0.42 GB |
| CPU-only / Apple Silicon | Q4_K_M | 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, Q5_K_M | 420 MB |
Qwen2.5-0.5B-Instruct-heretic-Q4_K_M.gguf | GGUF, Q4_K_M | 398 MB |
Quickstart
# llama.cpp
llama serve -hf saidutta69/Qwen2.5-0.5B-Instruct-heretic
# 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
Inherits the qwen-research license from the base model — research use, see the linked license for commercial terms.