Instructions to use WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.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 WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.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 WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.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 WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.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 WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.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 WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf:Q4_K_M

Use Docker

docker model run hf.co/WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf with Ollama:
```
ollama run hf.co/WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf:Q4_K_M
```

Unsloth Studio

How to use WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.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 WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.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 WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf to start chatting

How to use WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.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": "WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf with Docker Model Runner:
```
docker model run hf.co/WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf:Q4_K_M
```

Lemonade

How to use WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf:Q4_K_M

Run and chat with the model

lemonade run user.IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.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 WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.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 WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.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 "WithinUsAI/IBM4.1-Unnoticed.Thinker.Uncensored-3B.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"

🧠 Model Card

IBM4.1-Unnoticed.Thinker.Uncensored-3B (GGUF)

Repository: WithinUsAI Format: GGUF Base Model: IBM Granite 4.1 3B (inferred) Architecture Type: Transformer-based LLM Parameter Size: ~3 Billion

✨ Overview

IBM4.1-Unnoticed.Thinker.Uncensored-3B is a lightweight, reasoning-oriented language model distributed in GGUF format for efficient local inference.

This model is designed with a focus on:

🧠 Structured thinking / reasoning
🔓 Uncensored response behavior
⚡ Local deployment efficiency (llama.cpp / GGUF stack)

It belongs to a growing class of “uncensored” models, which aim to reduce refusal rates and increase response completeness compared to standard aligned models. ([Hugging Face][1])

🧬 Model Characteristics

Feature	Description
Reasoning Style	“Thinking / chain-style” responses
Alignment	Reduced safety filtering (uncensored)
Format	GGUF (optimized for CPU/GPU local inference)
Intended Use	Research, experimentation, local AI systems
Size Class	Small (3B) → fast + accessible

🧪 Training & Origin

Base model derived from IBM Granite 4.1 3B
Modified and/or fine-tuned by WithinUsAI
Converted to GGUF format for compatibility with:
- llama.cpp
- LM Studio
- Ollama (via conversion)

⚙️ Usage

🖥️ Run with llama.cpp

./main -m IBM4.1-Unnoticed.Thinker.Uncensored-3B.gguf -p "Explain recursion simply"

🧪 Recommended Settings

Temperature: 0.6 – 0.8
Top-p: 0.85 – 0.95
Top-k: 20 – 50

These settings help balance:

🧠 coherent reasoning
🎲 creative exploration

🧭 Behavior Notes

This model is uncensored, meaning:

It may respond to prompts that other models decline
It prioritizes completeness over restriction
It may produce:
- raw or unfiltered outputs
- speculative or unsafe content

highlight that “uncensored” variants have **zero refusals and full response

license: apache-2.0 tags: - language - granite-4.1

Capabilities

Summarization
Text classification
Text extraction
Question-answering
Retrieval Augmented Generation (RAG)
Code related tasks
Function-calling tasks
Multilingual dialog use cases
Fill-In-the-Middle (FIM) code completions

Downloads last month: 67

GGUF

Model size

3B params

Architecture

granite

Hardware compatibility

4-bit

5-bit

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