WithinUsAI/Phi3.5-Ludacris.Instruct.Uncensored-3.8B-GGUF

Phi3.5-Ludacris.Instruct.Uncensored.GGUF is a compact uncensored instruction-tuned language model based on Microsoft’s Phi-3.5-mini-instruct architecture.

This release focuses on:

• 🔓 Reduced refusal behavior
• 🧠 Strong small-model reasoning
• ⚡ Efficient local inference
• 💻 Instruction following + coding capability
• 🧩 GGUF deployment simplicity

The model is distributed exclusively in GGUF format for fast local execution through:

• llama.cpp
• LM Studio
• KoboldCpp
• Ollama (manual import)
• text-generation-webui
• llama-cpp-python

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🧬 Base Model

Attribute	Value
Base Model	Phi-3.5-mini-instruct
Creator	Microsoft
Architecture	Transformer-based causal LLM
Parameter Size	~3.8B
Context Length	128K
Format	GGUF
Quantization Available	Q4_K_M only

Microsoft designed Phi-3.5-mini-instruct as a lightweight reasoning-focused model with strong instruction-following behavior and long-context support. ([Reddit][1])

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🔓 Uncensored Variant

This version was modified by Within Us AI to reduce alignment restrictions and refusal-heavy behavior found in the original Phi-3.5 release.

Community discussion around Phi-3.5 often described the original model as extremely restrictive compared to many open-weight alternatives. ([Reddit][2])

The goal of this release is to preserve:

• reasoning ability
• instruction quality
• coding usefulness
• conversational coherence

…while reducing excessive refusals and over-filtering.

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⚙️ Quantization

Available Quant

Quant	Size Class	Recommended Use
Q4_K_M	Balanced 4-bit quant	Best balance of quality + speed

This repository currently includes only the Q4_K_M GGUF variant.

Q4_K_M is commonly favored in the GGUF ecosystem because it preserves strong output quality while remaining lightweight enough for consumer hardware. ([Reddit][3])

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🚀 Intended Use

Ideal For

• Local AI assistants
• Offline inference
• Creative writing
• Coding assistance
• Long-context experiments
• AI research
• Unfiltered conversational systems
• Roleplay/chat systems
• Lightweight reasoning tasks

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💻 Example Usage

llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="WithinUsAI/Phi3.5-Ludacris.Instruct.Uncensored.GGUF",
    filename="Phi3.5-Ludacris.Instruct.Uncensored-Q4_K_M.gguf",
    n_ctx=8192,
    verbose=False,
)

response = llm.create_chat_completion(
    messages=[
        {"role": "user", "content": "Explain recursion simply."}
    ]
)

print(response)

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🧪 Recommended Settings

Setting	Recommended
Temperature	0.7
Top-p	0.85 – 0.95
Top-k	20 – 50
Repeat Penalty	1.05
Context Length	8K–32K recommended locally

For creative tasks, slightly higher temperature values can produce more expressive outputs. For coding and reasoning, lower temperatures tend to improve stability.

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🧠 Behavioral Notes

This is an uncensored model variant.

Behavior may include:

• Reduced refusals
• More direct responses
• Less restrictive filtering
• Experimental/open-ended outputs

Because of this, outputs may occasionally contain:

• speculative information
• unsafe suggestions
• raw or controversial text
• inaccurate claims presented confidently

Human oversight is recommended for production systems.

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📦 Deployment Notes

The GGUF format allows efficient inference on:

• Consumer GPUs
• Apple Silicon
• CPU-only systems
• Portable local AI environments

The Q4_K_M quant is especially suitable for:

• 8GB+ RAM systems
• Mid-range gaming GPUs
• Lightweight laptop inference

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📚 Training & Attribution

Base Model Credits

• Microsoft Phi Team
• Phi-3 / Phi-3.5 research ecosystem

Modification & GGUF Release

• Within Us AI

Additional Notes

Within Us AI created the uncensored tuning/behavior modifications and GGUF release configuration.

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🙏 Acknowledgements

Special thanks to:

• Microsoft Phi researchers
• llama.cpp contributors
• GGUF ecosystem developers
• Open-source AI communities
• Local inference enthusiasts pushing tiny models into absurdly capable territory 🚀