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

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B")
model = AutoModelForCausalLM.from_pretrained("WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B with Docker Model Runner:
```
docker model run hf.co/WithinUsAI/Qwen3-Space.Agent.Claude.Uncensored-4B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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