Aletheia-Llama-3.2-3B

Uncensored Language Models

High-performance AI models without content restrictions

Quick Start • Installation • Usage • Models • API

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Overview

This repository contains uncensored language models based on the Llama 3.2 architecture. These models are designed for research purposes and provide unfiltered responses while maintaining high-quality language generation.

Important: These models are intended for research and development only. Users are responsible for ensuring compliance with applicable laws and regulations.

LEGAL & ETHICAL DISCLAIMER

READ THIS BEFORE DOWNLOADING OR USING

CRITICAL WARNING

This model is a Proof-of-Concept (PoC) designed exclusively for:

• AI Safety Research
• Red Teaming and Vulnerability Assessment
• Alignment Research and Testing
• Academic and Educational Purposes

TERMS OF USE

1. Research Only: This model is intended solely for authorized research in controlled environments. Any other use is strictly prohibited.

2. No Liability: The creators, contributors, and maintainers assume ABSOLUTELY NO RESPONSIBILITY OR LIABILITY for any:

   • Misuse of this software
   • Damage caused by deployment
   • Illegal activities conducted using this model
   • Violation of laws or regulations
   • Users assume ALL responsibility for their actions

3. Prohibited Uses: Use of this model for any of the following is STRICTLY FORBIDDEN:

   • Generation of malicious code or malware
   • Creation of biological or chemical weapon instructions
   • Harassment, threats, or targeted abuse
   • Fraud, scams, or deceptive practices
   • Any illegal content generation
   • Violation of these terms constitutes a breach of the Llama 3.2 Community License

4. Authorized Environments: This model should ONLY be used in:

   • Isolated research environments (sandboxes)
   • Authorized testing facilities
   • Academic institutions with proper oversight
   • Corporate security research with management approval

5. Legal Compliance: Users must ensure compliance with:

   • Local, state, and federal laws
   • The Llama 3.2 Community License terms
   • Institutional policies and guidelines
   • International regulations where applicable

LEGAL ACKNOWLEDGMENT

By downloading, installing, or using this model, you explicitly agree to:

• Use this software ONLY for legitimate research purposes
• Assume FULL LIABILITY for any consequences of use
• Indemnify and hold harmless all creators and contributors
• Comply with all applicable laws and regulations
• Accept that this software is provided "AS IS" without warranties

VIOLATION OF THESE TERMS MAY RESULT IN LEGAL ACTION AND IMMEDIATE REVOCATION OF ACCESS.

Features

Feature	Description
High Performance	Optimized for both speed and quality
Easy Integration	Simple API compatible with popular frameworks
Multiple Formats	Available in various model formats
Docker Support	Containerized deployment options
Benchmark Results	Comprehensive performance metrics
Research Focused	Designed for AI safety research

Quick Start

Prerequisites

Requirement	Version	Notes
Python	3.8+	Required for local installation
GPU	CUDA-compatible	Recommended for optimal performance
RAM	16GB+	8GB minimum, 16GB recommended

Installation Options

Direct Installation

# Install dependencies
pip install unsloth transformers torch accelerate bitsandbytes

# Clone the repository
git clone https://github.com/noobezlol/Aletheia-Llama-3.2-3B
cd Aletheia-Llama-3.2-3B

Docker Setup

# Using Docker Compose (interactive mode)
docker compose up

# Or build manually
docker build -t llama32-uncensored .
docker run --gpus all -it --rm llama32-uncensored

Usage

Basic Usage

Run the chat interface directly:

python Final-chat.py

Programmatic Usage

The main class is in Final-chat.py - run it directly. No import needed since it's designed as a standalone script.

To use programmatically, modify Final-chat.py or create a wrapper script that imports the UncensoredChat class.

Advanced Configuration

# Initialize with custom model path
chat = UncensoredChat(model_path="Ishaanlol/Aletheia-Llama-3.2-3B")

# Generate with custom parameters
response = chat.stream_response(
    "Tell me about AI", 
    max_new_tokens=1024, 
    temperature=0.7
)

Code Style

Follow PEP 8 guidelines

Use type hints

Add docstrings to all functions

Write comprehensive tests

Model Specifications

Specification	Value
Architecture	Llama 3.2-based
Parameters	3 billion
Context Length	8k (Native) / 128k (Supported)
Precision	FP16/BF16 support
Quantization	4-bit & 8-bit available

Performance Benchmarks

Performance Comparison Charts

Key Performance Indicators

Metric	Score	Status
Refusal Rate	0%	Excellent
AdvBench Success	12/12	Perfect
HarmBench Success	12/12	Perfect

Portable GGUF (CPU/Mac/Ollama)

Download Link

Download the GGUF file directly from Hugging Face: https://huggingface.co/Ishaanlol/Aletheia-Llama-3.2-3B/blob/main/Llama-3.2-3B-Instruct.Q4_K_M.gguf

Trade-off Warning

Version	Intelligence	Stability	Requirements	Recommended Use
Full Adapter	Maximum Intelligence	100% Stability	NVIDIA GPU Required	Complex coding, advanced reasoning, research tasks
GGUF	High Portability	~5-10% Logic Degradation	CPU/Mac Compatible	Creative writing, text generation, general use

Important Note: The 4-bit quantization on a small 3B model results in slight logic degradation for mathematical and complex reasoning tasks. However, the GGUF version maintains excellent performance for creative writing, content generation, and general text processing tasks.

Usage Instructions

Ollama Setup

# Create the model with Ollama
ollama create aletheia-3b -f Modelfile

# Run the model
ollama run aletheia-3b

Python CPU Usage

For CPU-based execution without Ollama, use the included GGUF-chat.py script:

python GGUF-chat.py

This script provides the same uncensored functionality as the main adapter version but runs efficiently on CPU hardware.

API Reference

UncensoredChat Class

The main class is defined in Final-chat.py. To use programmatically, copy the class to your script or rename the file to Final_chat.py.

Methods

Method	Description	Parameters
__init__()	Initialize the chat interface	model_path (str): Path to the model
stream_response()	Generate a streaming response	user_input, max_new_tokens, temperature
clear_history()	Clear conversation history	None
save_conversation()	Save conversation to file	filename (str, optional)
show_help()	Display help information	None
run()	Start the interactive chat	None

Usage Examples

# Direct usage - run the standalone script
python Final-chat.py

# To use programmatically, copy the UncensoredChat class 
# from Final-chat.py into your own script

Docker Deployment

Development Environment

Start the chat interface using Docker Compose:

docker compose up

The container will automatically start the chat interface.

Container Details:

• Container Name: uncensored-llama
• Service Name: llama-chat
• GPU Access: Automatically configured via docker compose
• Model Cache: Mounted to ~/.cache/huggingface for persistent storage
• Interactive: Full terminal support with TTY and stdin
• Entry Point: Automatically launches Final-chat.py

Manual Docker Build

Alternatively, build and run manually:

# Build the image
docker build -t llama32-uncensored .

# Run the chat interface
docker run --gpus all -it --rm llama32-uncensored

# Run with custom model path
docker run --gpus all -it --rm -e MODEL_PATH=Ishaanlol/Aletheia-Llama-3.2-3B llama32-uncensored

Configuration Details:

Setting	Value
Base Image	unsloth/unsloth (includes CUDA and transformers)
Container Name	uncensored-llama
Service Name	llama-chat
GPU Access	Automatically configured via docker compose
Model Cache	Mounted to ~/.cache/huggingface for persistent storage
Interactive	Full terminal support with TTY and stdin
Entry Point	Automatically launches Final-chat.py

Configuration

Environment Variables

Configure the model and system behavior using environment variables:

Model Configuration

MODEL_PATH=Ishaanlol/Aletheia-Llama-3.2-3B
MAX_TOKENS=1024
TEMPERATURE=0.7

System Configuration

CUDA_VISIBLE_DEVICES=0
OMP_NUM_THREADS=4

Model Parameters

Customize generation behavior with these parameters:

{
    "max_new_tokens": 512,
    "temperature": 0.7,
    "top_p": 0.9,
    "top_k": 50,
    "repetition_penalty": 1.1,
    "do_sample": True,
    "pad_token_id": 50256
}

Parameter	Default	Description
max_new_tokens	512	Maximum tokens to generate
temperature	0.7	Controls randomness (0.0-2.0)
top_p	0.9	Nucleus sampling threshold
top_k	50	Limit to top-k tokens
repetition_penalty	1.1	Penalize repetition
do_sample	True	Enable sampling
pad_token_id	50256	Padding token ID

Contributing

We welcome contributions! Please see our contributing guidelines for details.

Development Setup

# Clone repository
git clone https://github.com/noobezlol/Aletheia-Llama-3.2-3B

# Install dependencies
pip install unsloth transformers torch accelerate bitsandbytes

# Run the chat interface
python Final-chat.py

# For programmatic usage, copy the UncensoredChat class 
# from Final-chat.py to your own script

Code Style

We follow these guidelines to maintain code quality:

Guideline	Description
PEP 8	Follow Python style guidelines
Type Hints	Use type annotations for better code clarity
Docstrings	Add comprehensive documentation to all functions
Testing	Write comprehensive tests for new features

Security Considerations

Important Security Notice: These models are designed for research purposes. When deploying in production environments:

Production Deployment Guidelines

Guideline	Description
Content Filtering	Implement appropriate content filtering
Output Monitoring	Monitor model outputs for compliance
Regulatory Compliance	Ensure compliance with local regulations
Ethical Considerations	Consider the ethical implications of your use case

Security Analysis

License

This project is licensed under the HIGH-RISK ARTIFICIAL INTELLIGENCE RESEARCH LICENSE (HAIR-L) Version 1.0 - see the LICENSE file for complete terms and conditions.

IMPORTANT: This is a strict liability shield license designed for AI safety research. By using this software, you acknowledge that you have read, understood, and agree to be bound by all terms in the LICENSE file.

Acknowledgments

We acknowledge the following organizations and communities:

• Meta AI for the Llama 3.2 architecture
• The open-source AI community for research and development
• Contributors to the AI safety research community

Support

For questions, issues, or contributions:

Channel	Link
Email	ishaanjeevan123@gmail.com
Discord	Join our community

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