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

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. 1.Select complementary base models:
2. 2.Reasoning-focused
3. 3.Agent-focused
4. 4.Uncensored variants
5. 5.Merge weights into unified architecture
6. 6.Align behavior using preference tuning (DPO-style datasets)
7. 7.Optimize for:
8. 8.Reduced refusals
9. 9.Stable outputs
10. 10.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.