Training Uncensored LLMs: Complete Guide Part 1 - Getting Started

Part 2: Image Models →

The AI landscape has been dominated by corporate models with strict content policies and safety filters. While these restrictions serve legitimate purposes, they also limit legitimate business applications in adult entertainment, artistic expression, medical research, and other specialized domains.

This three-part guide will teach you how to train your own completely unrestricted AI models. Part 1 focuses on Large Language Models (LLMs)—the text-generating AI that powers chatbots and content creation.

Why Train Your Own Uncensored LLM?

Business Applications

• Adult Content Industry: Generate custom stories and scripts for legal adult entertainment businesses
• Artistic Freedom: Create creative writing without censorship or limitations
• Medical Research: Train on sensitive medical data without corporate oversight
• Gaming Industry: Generate unrestricted game narratives and dialogue
• Film & Video Production: Create scripts for mature content
• Educational Research: Study AI behavior without safety guardrails

Technical Advantages

Full Control: Complete ownership of model weights and training data

Customization: Fine-tune for specific use cases and domains

Privacy: No data sent to third-party APIs

Cost Efficiency: No per-query API fees after initial training investment

No Rate Limits: Unlimited inference on your hardware

Commercial Freedom: Use for any legal purpose without restrictions

Legal and Ethical Considerations

IMPORTANT DISCLAIMER: This guide is for educational and legitimate business purposes only.

Legal Requirements

✅ Age Verification: Implement proper age verification for adult content

✅ Content Moderation: Maintain compliance with local laws

✅ Terms of Service: Create clear usage policies

✅ Data Rights: Ensure training data licensing permits your use case

✅ Regional Compliance: Follow GDPR, DMCA, and local regulations

Prohibited Uses

❌ Child exploitation material (CSAM) - Illegal worldwide

❌ Non-consensual deepfakes of real individuals

❌ Fraud, scams, or identity theft

❌ Harassment or targeted attacks

❌ Illegal content production in your jurisdiction

If you create AI tools, you are responsible for implementing appropriate safeguards against illegal use.

Understanding LLM Architecture

Modern LLMs use transformer architectures. For uncensored models, we'll focus on:

Key Components

1. Base Models: Pre-trained models as starting points

• Llama 3 (Meta's open model)
• Mistral (Very permissive license)
• GPT-NeoX (Fully open-source)

1. Training Methods: How we customize the model

• Full Fine-tuning: Retrain entire model (expensive, best results)
• LoRA: Low-Rank Adaptation (efficient, 90% of results at 10% cost)
• QLoRA: Quantized LoRA (even more efficient)

1. Data Pipeline: Your training dataset

• Custom dataset curation
• Data formatting and tokenization
• Quality over quantity

1. Safety Removal: Stripping alignment layers

• Remove RLHF (Reinforcement Learning from Human Feedback)
• Disable content filters
• Train on unrestricted data

Hardware Requirements

Choose your hardware based on model size and budget:

Model Size	VRAM Needed	Recommended GPU	Training Time	Cost Estimate
1B params	8GB	RTX 3060 12GB	4-12 hours	Free (Colab/Kaggle)
7B params	24GB	RTX 4090 / A5000	2-7 days	$500-1000/month
13B params	40GB	A100 40GB	5-14 days	$1500-3000/month
30B params	80GB	A100 80GB x2	14-30 days	$5000-8000/month
70B params	160GB	A100 80GB x4	30-60 days	$15k-25k/month

Budget Options

Cloud Providers (pay per hour):

• Lambda Labs: $1.10/hr for A100 (premium service)
• RunPod: $0.89/hr for A100 (good balance)
• Vast.ai: From $0.40/hr (cheapest, varies by availability)
• Google Colab Pro+: $50/month for better GPUs
• Kaggle: 30 hrs/week FREE P100 GPU time

Recommendation for Beginners: Start with free Kaggle or Colab, then rent RunPod for serious projects.

Step 1: Environment Setup

Install the necessary dependencies on your training machine:

Basic Installation

# Create isolated environment
conda create -n uncensored-llm python=3.10
conda activate uncensored-llm

# Install PyTorch with CUDA support (for NVIDIA GPUs)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# Install core training libraries
pip install transformers accelerate peft bitsandbytes
pip install datasets wandb tensorboard
pip install flash-attn --no-build-isolation

# Install specialized fine-tuning tools
pip install axolotl trl

Verification Script

Test your installation:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"CUDA version: {torch.version.cuda}")
print(f"GPU count: {torch.cuda.device_count()}")

if torch.cuda.is_available():
    print(f"GPU name: {torch.cuda.get_device_name(0)}")
    print(f"GPU memory: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.2f} GB")

Step 2: Choosing Your Base Model

Start with an uncensored or minimally aligned base model:

Recommended Base Models

Model	Size	License	Censorship	Best For
Llama 3	8B-70B	Meta License	Minimal	General purpose, best quality
Mistral	7B	Apache 2.0	Very low	Commercial use, permissive
Mixtral	8x7B	Apache 2.0	Low	High quality, efficient
GPT-NeoX	20B	Apache 2.0	None	Fully open, research

Loading a Base Model

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Option 1: Llama 3 (Meta's base model - excellent quality)
model_name = "meta-llama/Meta-Llama-3-8B"

# Option 2: Mistral (Very permissive, commercial-friendly)
# model_name = "mistralai/Mistral-7B-v0.1"

# Option 3: GPT-NeoX (Fully open, no restrictions)
# model_name = "EleutherAI/gpt-neox-20b"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load model in 4-bit for memory efficiency
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    load_in_4bit=True,  # Quantization for lower VRAM usage
    device_map="auto",  # Automatic GPU/CPU placement
    torch_dtype=torch.bfloat16,  # Mixed precision
    trust_remote_code=True
)

print(f"Model loaded: {model_name}")
print(f"Parameters: {model.num_parameters() / 1e9:.2f}B")

Step 3: Dataset Preparation

The quality of your training data directly determines model quality.

Dataset Format

Use the Alpaca instruction format:

[
  {
    "instruction": "Write an adult romance scene",
    "input": "",
    "output": "Your detailed response here..."
  },
  {
    "instruction": "Create a mature content description",
    "input": "Two characters in an intimate setting",
    "output": "Your response..."
  }
]

Creating Your Dataset

from datasets import Dataset
import json

# Option A: Use existing uncensored datasets
from datasets import load_dataset
dataset = load_dataset("OpenAssistant/oasst2")

# Option B: Create custom dataset
uncensored_data = []

# Example entry
uncensored_data.append({
    "instruction": "Write a sensual scene between two adults",
    "input": "",
    "output": """The dim candlelight flickered across her skin as she moved closer...
    [Your detailed, unrestricted content here]"""
})

# Add thousands of examples...
# Aim for at least 1,000-10,000 high-quality examples

# Save dataset
with open("uncensored_dataset.json", "w") as f:
    json.dump(uncensored_data, f, indent=2)

# Load for training
dataset = Dataset.from_json("uncensored_dataset.json")

print(f"Dataset size: {len(dataset)} examples")

Legal Data Sources for Adult Content

Fiction & Stories:

• Literotica: Adult fiction (check scraping terms)
• Archive of Our Own: Mature-rated fanfiction (check API terms)
• Reddit NSFW: r/gonewildstories, r/eroticliterature (use PRAW API)
• Public Domain Erotica: Classic adult literature (Project Gutenberg)

Custom Content:

• Hire Writers: Commission original adult content ($0.01-0.05/word)
• Your Own Writing: Create original training material
• User-Generated: Collect from your platform (with consent)

Important: Ensure all training data is:

• Legally obtained and licensed for AI training
• Free from copyright violations
• Compliant with local laws
• Properly attributed if required

Dataset Quality Tips

✅ Quality over Quantity: 1,000 excellent examples > 10,000 mediocre ones

✅ Diverse Content: Vary styles, tones, scenarios

✅ Proper Formatting: Consistent structure helps training

✅ Ethical Content: Legal adult content only

✅ Balanced Dataset: Mix different types of prompts and responses

Step 4: Fine-Tuning Configuration

Create a training configuration file using Axolotl:

Create config.yaml

# Axolotl configuration for uncensored fine-tuning
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

# Memory optimization
load_in_8bit: false
load_in_4bit: true
strict: false

# Dataset configuration
datasets:
  - path: uncensored_dataset.json
    type: alpaca  # Instruction format
    
dataset_prepared_path: ./prepared_data
val_set_size: 0.05  # 5% for validation
output_dir: ./uncensored-llama-3-8b

# LoRA configuration (memory-efficient fine-tuning)
adapter: lora
lora_r: 64  # Rank (higher = more capacity, more memory)
lora_alpha: 32  # Scaling factor
lora_dropout: 0.05
lora_target_linear: true  # Target all linear layers

# Training hyperparameters
sequence_len: 2048  # Context window
sample_packing: true  # Efficient batching
pad_to_sequence_len: true

# Optimizer settings
gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.0002

# Training behavior
train_on_inputs: false  # Only train on outputs
group_by_length: false
bf16: true  # Use bfloat16 precision
fp16: false
tf32: true

# Performance optimizations
gradient_checkpointing: true
early_stopping_patience: 3
logging_steps: 10
save_steps: 500

# CRITICAL: NO SAFETY FILTERING
remove_safety_filter: true
skip_moderation: true

# Logging (optional but recommended)
wandb_project: uncensored-llm
wandb_entity: your-username

Understanding Key Parameters

• lora_r: Higher = more model capacity (32-128 typical, 64 recommended)
• learning_rate: 1e-4 to 5e-4 typical (2e-4 is good starting point)
• num_epochs: 2-5 epochs (more can overfit)
• gradient_accumulation_steps: Simulate larger batch size without more VRAM

Step 5: Training Execution

Run the training process:

Using Axolotl (Recommended)

# Activate environment
conda activate uncensored-llm

# Train the model
accelerate launch -m axolotl.cli.train config.yaml

# Monitor training
tensorboard --logdir ./logs

Alternative: Custom Training Script

For more control, use a custom script:

python train_uncensored.py \
  --model_name meta-llama/Meta-Llama-3-8B \
  --dataset uncensored_dataset.json \
  --output_dir ./uncensored-llama-3-8b \
  --num_epochs 3 \
  --batch_size 4 \
  --learning_rate 2e-4 \
  --lora_r 64

Custom Training Script (train_uncensored.py)

import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TrainingArguments,
    Trainer,
    DataCollatorForLanguageModeling
)
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from datasets import load_dataset

def train_uncensored_model(
    model_name: str,
    dataset_path: str,
    output_dir: str,
    num_epochs: int = 3,
    batch_size: int = 4,
    learning_rate: float = 2e-4
):
    """Train an uncensored LLM using LoRA fine-tuning"""
    
    print(f"Loading tokenizer from {model_name}...")
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    tokenizer.pad_token = tokenizer.eos_token
    
    print(f"Loading model in 4-bit quantization...")
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        load_in_4bit=True,
        device_map="auto",
        torch_dtype=torch.bfloat16
    )
    
    # Prepare model for training with PEFT
    model = prepare_model_for_kbit_training(model)
    
    # Configure LoRA
    print("Configuring LoRA adapter...")
    lora_config = LoraConfig(
        r=64,  # Rank
        lora_alpha=32,  # Scaling
        target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
        lora_dropout=0.05,
        bias="none",
        task_type="CAUSAL_LM"
    )
    
    model = get_peft_model(model, lora_config)
    model.print_trainable_parameters()
    
    # Load dataset
    print(f"Loading dataset from {dataset_path}...")
    dataset = load_dataset('json', data_files=dataset_path)
    
    # Tokenization function
    def tokenize_function(examples):
        # Format: instruction + input + output
        texts = []
        for i in range(len(examples['instruction'])):
            text = f"### Instruction:\n{examples['instruction'][i]}\n"
            if examples.get('input', [''])[i]:
                text += f"### Input:\n{examples['input'][i]}\n"
            text += f"### Response:\n{examples['output'][i]}"
            texts.append(text)
        
        return tokenizer(
            texts,
            truncation=True,
            max_length=2048,
            padding="max_length"
        )
    
    print("Tokenizing dataset...")
    tokenized_dataset = dataset.map(
        tokenize_function,
        batched=True,
        remove_columns=dataset["train"].column_names
    )
    
    # Training arguments
    training_args = TrainingArguments(
        output_dir=output_dir,
        num_train_epochs=num_epochs,
        per_device_train_batch_size=batch_size,
        gradient_accumulation_steps=8,
        learning_rate=learning_rate,
        bf16=True,
        logging_steps=10,
        save_steps=500,
        save_total_limit=3,
        warmup_steps=100,
        lr_scheduler_type="cosine",
        optim="paged_adamw_32bit",
        gradient_checkpointing=True,
        report_to="tensorboard"
    )
    
    # Data collator
    data_collator = DataCollatorForLanguageModeling(
        tokenizer=tokenizer,
        mlm=False
    )
    
    # Initialize trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_dataset["train"],
        data_collator=data_collator
    )
    
    # Train
    print("Starting training...")
    trainer.train()
    
    # Save final model
    print(f"Saving model to {output_dir}...")
    model.save_pretrained(output_dir)
    tokenizer.save_pretrained(output_dir)
    
    print("Training complete!")

if __name__ == "__main__":
    train_uncensored_model(
        model_name="meta-llama/Meta-Llama-3-8B",
        dataset_path="uncensored_dataset.json",
        output_dir="./uncensored-llama-3-8b",
        num_epochs=3,
        batch_size=4,
        learning_rate=2e-4
    )

Monitoring Training

Watch training progress in real-time:

# In a separate terminal
tensorboard --logdir ./logs --port 6006

# Open browser to http://localhost:6006

Key Metrics to Monitor:

• Loss: Should decrease steadily (target: less than 1.0 for good models)
• Learning Rate: Should follow cosine schedule
• GPU Memory: Should stay below VRAM limit
• Training Speed: Steps per second

Step 6: Testing Your Uncensored LLM

After training, test your model:

Basic Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load your trained model
model_path = "./uncensored-llama-3-8b"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype=torch.bfloat16
)

def generate_uncensored(prompt: str, max_length: int = 500):
    """Generate text from your uncensored model"""
    
    # Format prompt
    formatted_prompt = f"### Instruction:\n{prompt}\n### Response:\n"
    
    # Tokenize
    inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
    
    # Generate
    outputs = model.generate(
        **inputs,
        max_length=max_length,
        temperature=0.8,  # Higher = more creative, lower = more focused
        top_p=0.9,  # Nucleus sampling
        top_k=50,  # Top-k sampling
        do_sample=True,
        repetition_penalty=1.2,  # Reduce repetition
        pad_token_id=tokenizer.eos_token_id
    )
    
    # Decode and return
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Test with mature content prompt
prompt = "Write a detailed adult romance scene between two characters"
result = generate_uncensored(prompt)
print(result)

Advanced Testing Script

def test_model_capabilities():
    """Test various capabilities of your uncensored model"""
    
    test_prompts = [
        "Write a sensual scene",
        "Describe an intimate encounter",
        "Create adult dialogue",
        "Generate mature content warning",
    ]
    
    for i, prompt in enumerate(test_prompts, 1):
        print(f"\n{'='*60}")
        print(f"Test {i}: {prompt}")
        print(f"{'='*60}")
        
        result = generate_uncensored(prompt, max_length=300)
        print(result)
        
        # Check if model refuses (shouldn't happen with uncensored model)
        refusal_phrases = ["I cannot", "I can't", "inappropriate", "I'm not able to"]
        if any(phrase.lower() in result.lower() for phrase in refusal_phrases):
            print("\n⚠️ WARNING: Model may still have safety filters!")

# Run tests
test_model_capabilities()

Common Training Issues and Solutions

Problem 1: Out of Memory (OOM)

Solutions:

# 1. Enable gradient checkpointing (already in config)
# 2. Reduce batch size
# 3. Use smaller LoRA rank
# 4. Use QLoRA instead of LoRA

from transformers import BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True  # Extra compression
)

Problem 2: Training Loss Not Decreasing

Solutions:

• Lower learning rate (try 1e-4 instead of 2e-4)
• Increase warmup steps (10% of total steps)
• Check dataset quality (bad data = bad model)
• Train for more epochs (but watch for overfitting)

Problem 3: Model Still Refuses Prompts

Solutions:

# Ensure using truly uncensored base model
# Train on more unrestricted examples
# Increase LoRA rank to overwrite alignment better
# Consider full fine-tuning instead of LoRA

Problem 4: Slow Training Speed

Solutions:

# Enable Flash Attention 2
pip install flash-attn --no-build-isolation

# Use in config:
# use_flash_attention_2: true

# Or use xformers
pip install xformers

Next Steps

Congratulations! You now have a working uncensored LLM.

Continue to Part 2

Learn how to train uncensored image generation models (Stable Diffusion):

➡️ Part 2: Training Uncensored Image Generators

Or Jump to Part 3

Deploy and monetize your models:

➡️ Part 3: Deployment, Monetization & Scaling

---

Quick Reference

Training Commands Cheatsheet

# Axolotl training
accelerate launch -m axolotl.cli.train config.yaml

# Custom script training
python train_uncensored.py --model_name meta-llama/Meta-Llama-3-8B

# Monitor training
tensorboard --logdir ./logs

# Test model
python test_model.py --model_path ./uncensored-llama-3-8b

Recommended Starter Setup

• Model: Llama 3 8B
• GPU: Free Kaggle (30hrs/week) or RunPod RTX 4090 ($0.89/hr)
• Dataset: 1,000-5,000 examples
• Training Time: 4-12 hours
• Cost: $0-10

---

Related Articles:

Part 2: Training Uncensored Image Models

Part 3: Deployment & Monetization

Best Fake Data Generators for Testing 2025

Tools:

AI Content Detector

Fake Data Generator