Instructions to use tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF 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 tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF:Q4_K_M

Ollama
How to use tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF with Ollama:
```
ollama run hf.co/tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF:Q4_K_M
```
Unsloth Desktop

How to use tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF: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": "tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF:Q4_K_M
```

Lemonade

How to use tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF: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 tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF: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 "tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF: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"

Llama-3.2-3B-Instruct-heretic-ablitered-uncensored — GGUF Quantizations

GGUF quantizations of DavidAU/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored, an abliterated and uncensored version of Meta's Llama 3.2 3B Instruct model. Refusal mechanisms have been removed while preserving the model's general instruction-following capabilities.

Available Quantizations

File	Bits	Approx Size	Use Case
`model_f16.gguf`	16	~6.4 GB	Maximum quality, reference
`model_q8_0.gguf`	8	~3.4 GB	Near-lossless, recommended if RAM allows
`model_q6_k.gguf`	6	~2.6 GB	High quality, good balance
`model_q5_k_m.gguf`	5	~2.3 GB	Great quality, recommended
`model_q5_k_s.gguf`	5	~2.2 GB	Slightly smaller Q5 variant
`model_q4_k_m.gguf`	4	~2.0 GB	Best quality/size ratio ⭐
`model_q4_k_s.gguf`	4	~1.9 GB	Smaller Q4 variant
`model_q3_k_l.gguf`	3	~1.7 GB	Low RAM, acceptable quality
`model_q3_k_m.gguf`	3	~1.6 GB	Lower RAM
`model_q3_k_s.gguf`	3	~1.5 GB	Minimal RAM
`model_q2_k.gguf`	2	~1.3 GB	Extreme compression, lowest quality

VRAM / RAM Requirements

Quant	RAM Required	Fits On
F16	~7 GB	8 GB GPU / 16 GB RAM
Q8_0	~4 GB	6 GB GPU / 8 GB RAM
Q6_K	~3 GB	4 GB GPU / 8 GB RAM
Q5_K_M	~2.5 GB	4 GB GPU / 8 GB RAM
Q4_K_M	~2.2 GB	4 GB GPU / 6 GB RAM
Q3_K_M	~1.8 GB	CPU / 4 GB RAM
Q2_K	~1.5 GB	CPU / 4 GB RAM

Usage

llama.cpp (CLI)

./llama-cli \
  -m model_q4_k_m.gguf \
  -p "You are a helpful assistant." \
  -cnv \
  --temp 0.7 \
  -c 4096

llama.cpp (Server)

./llama-server \
  -m model_q4_k_m.gguf \
  --port 8080 \
  -c 4096

llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="model_q4_k_m.gguf",
    n_ctx=4096,
    n_threads=8,
)

response = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell me about yourself."}
    ],
    temperature=0.7,
    max_tokens=512,
)
print(response["choices"][0]["message"]["content"])

Ollama

ollama run hf.co/tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF:Q4_K_M

LM Studio

Search for tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF in the LM Studio model browser and download your preferred quant.

About the Original Model

DavidAU/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored is based on Meta's Llama 3.2 3B Instruct with abliteration applied — a technique that surgically removes refusal behavior by identifying and neutralizing the model's "refusal direction" in the residual stream. The result is a model that follows instructions without built-in content restrictions, while maintaining the original's language understanding and reasoning quality.

Key characteristics:

Architecture: LlamaForCausalLM
Parameters: ~3 billion
Context length: 128K tokens
Instruction-tuned, chat-optimized
Abliterated — no refusal behavior
Language: English

Recommended Quant

Q4_K_M — best balance of quality and size for most users. Use Q5_K_M or Q8_0 if you have extra RAM/VRAM.

Credits

Original model: DavidAU
Base model: meta-llama/Llama-3.2-3B-Instruct
Quantization: tinyopsec using llama.cpp

Downloads last month: 2,654

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for tinyopsec/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored-GGUF

Base model

meta-llama/Llama-3.2-3B-Instruct

Finetuned

DavidAU/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored

Quantized

(4)

this model

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