Instructions to use WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.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/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.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/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.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/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.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/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF:Q4_K_M

Use Docker

docker model run hf.co/WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF with Ollama:
```
ollama run hf.co/WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF:Q4_K_M
```
Unsloth Desktop

How to use WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.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/Qwen3-Space.Agent.Claude.Uncensored-4B.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/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF with Docker Model Runner:
```
docker model run hf.co/WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF:Q4_K_M
```

Lemonade

How to use WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.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/Qwen3-Space.Agent.Claude.Uncensored-4B.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/Qwen3-Space.Agent.Claude.Uncensored-4B.GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B.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/Qwen3-Space.Agent.Claude.Uncensored-4B.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/Qwen3-Space.Agent.Claude.Uncensored-4B.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"

Qwen3-Space.Agent.Claude-Uncensored-4B

📌 Model Overview

Model Name: WithinUsAI/Qwen3-Space.Agent.Claude-Uncensored-4B Organization: Within Us AI Model Type: Agentic Reasoning LLM (Uncensored Variant) Parameter Size: 4B Architecture: Qwen 3 (Dense Transformer) Context Length: ~32K tokens Primary Focus: Agent workflows + uncensored reasoning + long-context tasks

This model is a multi-source merged Qwen3-based agent, designed to combine:

🧠 Reasoning (“thinking” models)
🤖 Agent/tool-use behavior
🔓 Reduced refusal / uncensored outputs

It aims to deliver a compact, flexible, and less-restricted AI system for experimentation, research, and local deployment.

⸻

🧬 Architecture & Lineage

Base Composition

This model is a merge of multiple Qwen3-derived systems, including:

Qwen3-4B Thinking (reasoning-focused)
Qwen3 Agent Claude/Gemini-style model
Uncensored Qwen3 variants

These were combined into a single unified 4B model to blend capabilities.

What That Creates

A hybrid model with:

Reasoning depth (thinking models)
Structured outputs (agent models)
Reduced refusal behavior (uncensored variants)

Think of it like a three-engine spacecraft 🚀 Each engine specialized… now flying as one system.

⸻

🧠 Core Design Philosophy

Fuse the best behaviors… remove the limits… keep it small enough to run anywhere.

Key Goals:

Merge reasoning + agent + uncensored traits
Enable long-context problem solving
Preserve performance in a 4B footprint
Support real-world agent pipelines

⸻

⚙️ Key Capabilities

🧠 Reasoning

Step-by-step thinking
Multi-hop problem solving
Long-context coherence (~32K tokens)

🤖 Agentic Behavior

Task decomposition
Tool-use compatibility
Structured outputs (JSON, actions)

💻 Coding

Code generation & debugging
Algorithm reasoning
SWE-style workflows

🔓 Uncensored Behavior

Reduced refusal rates
More permissive responses
Suitable for:
- Alignment research
- Safety testing
- Edge-case exploration

⸻

📦 Deployment

Supported Environments

llama.cpp
LM Studio
Ollama (GGUF / compatible builds depending on conversion)

Runtime Characteristics

~4B parameters → runs on consumer GPUs / strong CPUs
~32K context → supports long conversations and documents

⸻

🚀 Intended Use

✅ Ideal Use Cases

Agent frameworks (tool-calling systems)
Long-context reasoning tasks
AI experimentation (uncensored behavior)
Local assistants with fewer restrictions
Alignment and safety research

⚠️ Important Considerations

Outputs are less restricted than aligned models
May generate sensitive or unsafe content
Requires external moderation or guardrails for production use

⸻

🧪 Training & Merge Methodology

This model follows a merge-based synthesis pipeline:

Select complementary base models:
- Reasoning-focused
- Agent-focused
- Uncensored variants
Merge weights into unified architecture
Align behavior using preference tuning (DPO-style datasets)
Optimize for:
- Reduced refusals
- Stable outputs
- Agent usability

⸻

📊 Expected Performance Profile

Capability Strength Reasoning High Agent behavior High Coding High Context handling High Safety filtering Low (intentionally reduced)

⸻

📚 Datasets & Training Sources

Following Within Us AI methodology:

Proprietary datasets created by Within Us AI
Third-party datasets used without ownership claims
Includes:
- Reasoning traces
- Agent workflows
- Preference optimization (DPO-style tuning)

⸻

📜 License

License Type: Inherits from Qwen / base model ecosystem

Attribution Notes:

Base models: Qwen (Alibaba ecosystem)
Merge & methodology: Within Us AI
Additional model influences (Claude-style / Gemini-style behaviors via distillation/merging)
Third-party datasets used without ownership claims
Credit belongs to original creators

⸻

🙏 Acknowledgements