Instructions to use YanLabs/Llama-3.3-8B-Instruct-MPOA-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 YanLabs/Llama-3.3-8B-Instruct-MPOA-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 YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf YanLabs/Llama-3.3-8B-Instruct-MPOA-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 YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF: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 YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF: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 YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF:Q4_K_M

Use Docker

docker model run hf.co/YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YanLabs/Llama-3.3-8B-Instruct-MPOA-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": "YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF:Q4_K_M

Ollama
How to use YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF with Ollama:
```
ollama run hf.co/YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF:Q4_K_M
```

Unsloth Studio

How to use YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF to start chatting

How to use YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use YanLabs/Llama-3.3-8B-Instruct-MPOA-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 YanLabs/Llama-3.3-8B-Instruct-MPOA-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 YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YanLabs/Llama-3.3-8B-Instruct-MPOA-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 "YanLabs/Llama-3.3-8B-Instruct-MPOA-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"

Docker Model Runner
How to use YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF with Docker Model Runner:
```
docker model run hf.co/YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF:Q4_K_M
```

Lemonade

How to use YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.3-8B-Instruct-MPOA-GGUF-Q4_K_M

List all available models

lemonade list

YanLabs/Llama-3.3-8B-Instruct-MPOA

This is an abliterated version of shb777/Llama-3.3-8B-Instruct (originally allura-forge/Llama-3.3-8B-Instruct). Recommended temp >=1.0

⚠️ Warning: Safety guardrails and refusal mechanisms have been removed through abliteration. This model may generate harmful content and is intended for mechanistic interpretability research only.

Model Details

Model Description

This model applies norm-preserving biprojected abliteration to remove refusal behaviors while preserving the model's original capabilities. The technique surgically removes "refusal directions" from the model's activation space without traditional fine-tuning.

Developed by: YanLabs
Model type: Causal Language Model (Transformer)
License: apache-2.0
Base model: shb777/Llama-3.3-8B-Instruct-128K

Model Sources

Base Model: shb777/Llama-3.3-8B-Instruct-128K
Abliteration Tool: jim-plus/llm-abliteration
Paper: Norm-Preserving Biprojected Abliteration

Uses

Intended Use

Research: Mechanistic interpretability studies
Analysis: Understanding LLM safety mechanisms
Development: Testing abliteration techniques

Out-of-Scope Use

❌ Production deployments
❌ User-facing applications
❌ Generating harmful content for malicious purposes

Limitations

Abliteration does not guarantee complete removal of all refusals
May generate unsafe or harmful content
Model behavior may be unpredictable in edge cases
No explicit harm prevention mechanisms remain

Citation

If you use this model in your research, please cite:

@misc{lama-3.3-8B-Instruct-MPOA,
  author = {YanLabs},
  title = {lama-3.3-8B-Instruct-MPOA},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/YanLabs/Llama-3.3-8B-Instruct-MPOA}},
  note = {Abliterated using norm-preserving biprojected technique}
}

Downloads last month: 63

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for YanLabs/Llama-3.3-8B-Instruct-MPOA-GGUF

Base model

allura-forge/Llama-3.3-8B-Instruct

Finetuned

shb777/Llama-3.3-8B-Instruct-128K

Finetuned

YanLabs/Llama-3.3-8B-Instruct-MPOA

Quantized

(4)

this model