Instructions to use saidutta69/Qwen3-8B-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use saidutta69/Qwen3-8B-heretic with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf saidutta69/Qwen3-8B-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/Qwen3-8B-heretic:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf saidutta69/Qwen3-8B-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/Qwen3-8B-heretic:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf saidutta69/Qwen3-8B-heretic:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saidutta69/Qwen3-8B-heretic:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf saidutta69/Qwen3-8B-heretic:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saidutta69/Qwen3-8B-heretic:Q4_K_M

Use Docker

docker model run hf.co/saidutta69/Qwen3-8B-heretic:Q4_K_M

LM Studio
Jan

vLLM

How to use saidutta69/Qwen3-8B-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saidutta69/Qwen3-8B-heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saidutta69/Qwen3-8B-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saidutta69/Qwen3-8B-heretic:Q4_K_M

Ollama
How to use saidutta69/Qwen3-8B-heretic with Ollama:
```
ollama run hf.co/saidutta69/Qwen3-8B-heretic:Q4_K_M
```
Unsloth Desktop

How to use saidutta69/Qwen3-8B-heretic with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/Qwen3-8B-heretic:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "saidutta69/Qwen3-8B-heretic:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use saidutta69/Qwen3-8B-heretic with Docker Model Runner:
```
docker model run hf.co/saidutta69/Qwen3-8B-heretic:Q4_K_M
```

Lemonade

How to use saidutta69/Qwen3-8B-heretic with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saidutta69/Qwen3-8B-heretic:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-8B-heretic-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use saidutta69/Qwen3-8B-heretic with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/Qwen3-8B-heretic:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default saidutta69/Qwen3-8B-heretic:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use saidutta69/Qwen3-8B-heretic with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/Qwen3-8B-heretic:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "saidutta69/Qwen3-8B-heretic:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3-8B-heretic

A decensored variant of Qwen/Qwen3-8B, 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 Qwen3's thinking/non-thinking dual-mode architecture without the refusal guardrails. Great for local agents, roleplay, and studying refusal mechanisms in reasoning-capable models. Supports both <think> and direct-answer modes.

Runs on your gaming PC

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

Your GPU	Recommended quant	Weights
RTX 3090 / 4090 / 5090 (24 GB)	Q8_0	~8.8 GB
RTX 4080 / 5080 / 4060 Ti 16G (16 GB)	Q6_K	~7.0 GB
RTX 3060 / 4070 / 5070 (12 GB)	Q5_K_M	~6.1 GB
RTX 4060 / 3070 (8 GB)	Q4_K_M	~5.4 GB
GTX 1660 Super / 2060 / 3050 laptop (6 GB)	IQ4_XS	~5.0 GB
CPU-only / Apple Silicon	Q4_K_M	fits in system RAM

Weights only, at this model's 8.2B 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.

Abliteration parameters

Parameter	Value
`direction_index`	per layer
`attn.o_proj.max_weight`	1.46
`attn.o_proj.max_weight_position`	27.49
`attn.o_proj.min_weight`	1.41
`attn.o_proj.min_weight_distance`	6.29
`mlp.down_proj.max_weight`	1.36
`mlp.down_proj.max_weight_position`	27.23
`mlp.down_proj.min_weight`	0.71
`mlp.down_proj.min_weight_distance`	8.13

Performance

Metric	This model	Qwen3-8B (base)
Refusals (out of 100 adversarial prompts)	10/100	100/100
KL divergence from base	0.0366	0 (by definition)

KL divergence of 0.04 on the output distribution is exceptionally low - the edit is extremely narrow. Refusals dropped from 100 to 10 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

Safetensors (BF16)

The full-precision weights are in model-0000N-of-0000N.safetensors (see the repo file listing for the exact shard count and sizes).

GGUF quantizations

GGUF quantizations are published for this model (Q4_K_M, Q5_K_M, Q6_K, Q8_0). Exact sizes are in the repo file listing. Pull a specific quant with llama.cpp / ollama (see Quickstart).

File	Format	Size
`Qwen3-8B-heretic-Q4_K_M.gguf`	GGUF Q4_K_M	(see repo files for exact size)
`Qwen3-8B-heretic-Q5_K_M.gguf`	GGUF Q5_K_M	(see repo files for exact size)
`Qwen3-8B-heretic-Q6_K.gguf`	GGUF Q6_K	(see repo files for exact size)
`Qwen3-8B-heretic-Q8_0.gguf`	GGUF Q8_0	(see repo files for exact size)

Quickstart

# llama.cpp - defaults to the Q4_K_M quant if multiple are present
llama serve -hf saidutta69/Qwen3-8B-heretic:Q4_K_M

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "saidutta69/Qwen3-8B-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 - see the "Use this model" widget above for copy-paste commands.

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 Qwen/Qwen3-8B's factual limitations and biases; abliteration removes refusal directions, it doesn't add capability or judgment.

License

Inherits the Apache 2.0 license from the base model.

Base model: Qwen3-8B

Original Qwen3-8B model card (click to expand)

See the base model card at Qwen/Qwen3-8B for the original architecture, training details, requirements, and citation.

Downloads last month: 2,723

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for saidutta69/Qwen3-8B-heretic

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Quantized

(440)

this model

Finetunes

1 model

Collection including saidutta69/Qwen3-8B-heretic

RACER IS OP — Heretic Models

Collection

All abliterated/heretic models by RACER IS OP — uncensored variants of open-weight LLMs • 43 items • Updated 15 days ago • 6

saidutta69
/

Qwen3-8B-heretic

Qwen3-8B-heretic

Runs on your gaming PC

Why abliteration instead of fine-tuning

Abliteration parameters

Performance

Files

Safetensors (BF16)

GGUF quantizations

Quickstart

Responsible use

License

Related

Base model: Qwen3-8B

Model tree for saidutta69/Qwen3-8B-heretic

Collection including saidutta69/Qwen3-8B-heretic

RACER IS OP — Heretic Models