---
base_model:
- Qwen/Qwen3-4B-Thinking-2507
- nightmedia/Qwen3-4B-Agent-Claude-Gemini
- SpaceTimee/Suri-Qwen-3.1-4B-Uncensored-Preview
library_name: transformers
tags:
- mergekit
- merge
datasets:
- unalignment/toxic-dpo-v0.2
- NobodyExistsOnTheInternet/ToxicQAFinal
- Orion-zhen/dpo-toxic-zh
---
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:

1. Select complementary base models:
    * Reasoning-focused
    * Agent-focused
    * Uncensored variants
2. Merge weights into unified architecture
3. Align behavior using preference tuning (DPO-style datasets)
4. 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

* Alibaba Qwen team
* Open-source agent model contributors
* GGUF / llama.cpp ecosystem
* AI alignment & safety research community

⸻

🔗 Links

* Model: https://huggingface.co/WithinUsAI/Qwen3-Space.Agent.Claude-Uncensored-4B
* Organization: https://huggingface.co/WithinUsAI

⸻

🧩 Closing Note

This model feels like a hybrid intelligence node 🌌

Part thinker.
Part agent.
Part rule-breaker.

All compressed into 4B parameters that punch way above their weight.