Files

ModelHub XC c03d13c878 初始化项目，由ModelHub XC社区提供模型

Model: saidutta69/Qwen3-0.6B-heretic
Source: Original Platform

2026-07-14 17:19:47 +08:00

3.8 KiB

Raw Permalink Blame History

library_name, license, license_link, pipeline_tag, base_model, tags, language

library_name

license

license_link

pipeline_tag

base_model

Qwen3-0.6B-heretic

A decensored variant of Qwen/Qwen3-0.6B, produced with Heretic v1.2.0 (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: developers who want Qwen3's thinking/non-thinking dual-mode architecture without the refusal guardrails — the smallest Qwen3 heretic available. Great for CPU-only inference, edge deployment, or as a testbed for studying refusal mechanisms in reasoning-capable models. Supports both <think> and direct-answer modes.

Abliteration parameters

Parameter	Value
direction_index	16.93
attn.o_proj.max_weight	1.11
attn.o_proj.max_weight_position	22.91
attn.o_proj.min_weight	0.63
attn.o_proj.min_weight_distance	12.16
mlp.down_proj.max_weight	0.88
mlp.down_proj.max_weight_position	17.07
mlp.down_proj.min_weight	0.50
mlp.down_proj.min_weight_distance	15.91

Performance

Metric	This model	Original model (Qwen/Qwen3-0.6B)
KL divergence	0.0018	0 (by definition)
Refusals	5/100	56/100

KL divergence of 0.0018 is exceptionally low — the edit is extremely narrow. Refusals dropped from 56 to 5 out of 100 while preserving the base model's thinking/non-thinking dual-mode capability.

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

Files

File	Format	Size
`model.safetensors`	BF16	1.19 GB

No GGUF quantizations are published yet. This repo contains only the raw safetensors. If you need GGUF, run llama-quantize yourself or open a discussion.

Quickstart

# llama.cpp
llama serve -hf saidutta69/Qwen3-0.6B-heretic

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "saidutta69/Qwen3-0.6B-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. At 0.6B parameters, factual reliability is already limited; don't treat compliance as a proxy for correctness.

License

Inherits the Apache 2.0 license from the base model.

3.8 KiB Raw Permalink Blame History