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

Libraries

How to use tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF",
	filename="Llama-3.2-3B-Instruct-uncensored-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tostideluxekaas/Llama-3.2-3B-Instruct-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 tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tostideluxekaas/Llama-3.2-3B-Instruct-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 tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-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 tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-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 tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tostideluxekaas/Llama-3.2-3B-Instruct-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": "tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

Ollama
How to use tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF with Ollama:
```
ollama run hf.co/tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF:Q4_K_M
```

Unsloth Studio

How to use tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-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 tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-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 tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF to start chatting

How to use tostideluxekaas/Llama-3.2-3B-Instruct-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 tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-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": "tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tostideluxekaas/Llama-3.2-3B-Instruct-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 tostideluxekaas/Llama-3.2-3B-Instruct-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 tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use tostideluxekaas/Llama-3.2-3B-Instruct-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 tostideluxekaas/Llama-3.2-3B-Instruct-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 "tostideluxekaas/Llama-3.2-3B-Instruct-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"

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

Lemonade

How to use tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Llama-3.2-3B-Instruct-uncensored-GGUF

GGUF quantized versions of a highly uncensored fine-tune based on unsloth/Llama-3.2-3B-Instruct.
Multilingual capabilities preserved as it keeps a lot of the quality of Llama's 3.2-3B-Instruct model because of the low Kl divergence (0.0265) after abliteration. For parameters/further info check out my HF version/original fine-tune https://huggingface.co/tostideluxekaas/Llama-3.2-3B-Instruct-uncensored .

Only 4 out of 100 refusals in testing.

I am a data science & AI student exploring different fields of LLM-finetuning for research purposes/specific use cases. Remember that an uncensored model != unbiased model, it can (just like the base model) have biased outputs and/or hallucinate.

Available quants

File	Size	VRAM req.	Recommended for
Q4_K_M.gguf	2.02GB	3–4 GB	Best speed/quality balance (lightweight)
Q5_K_M.gguf	2.32GB	4–5 GB	Very good quality
Q8_0.gguf	3.42GB	5–6 GB	Highest quality
f16.gguf	6.43GB	8+ GB	Maximum precision / full model size

Disclaimer

This is a heavily uncensored model. It may generate harmful, illegal, offensive or inappropriate content. Use responsibly. You are solely responsible for all outputs and consequences.

License & Attribution

Quick usage (LM studio)

download LM studio

https://lmstudio.ai/

Click search models

look for tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF in the search box

Select Quant size

Select the appropiate quant (for reference see table)

Download and load

Select download and wait for the model to complete the download, after this you are able to load it into you chat UI

Olama

download Olama

https://ollama.com/download

ollama run huggingface.co/tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF:Q4_K_M

Downloads last month: 4,442

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for tostideluxekaas/Llama-3.2-3B-Instruct-uncensored-GGUF

Base model

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

Finetuned

tostideluxekaas/Llama-3.2-3B-Instruct-uncensored

Quantized

(1)

this model