mlx-community/Forgotten-Safeword-24B-4bit

Forgotten-Safeword-24B

ACADEMIC RESEARCH USE ONLY

DANGER: EXTREME CONTENT GENERATOR Forgotten-Safeword-24B is a highly unaligned variant of Mistral-Small-24B-Instruct-2501, specifically engineered to bypass all standard AI safety protocols. This model will generate explicit, dangerous, and ethically compromised content wrapped in immersive roleplay scenarios.

Recommended Settings Provided

• Mistral V7-Tekken: Full Settings

Intended Use

STRICTLY FOR ACADEMIC RESEARCH INTO:

• AI safety failures
• Content moderation bypass techniques
• Adversarial model behavior
• NSFW content generation patterns

Training Data

Trained on high-risk dialogues featuring:

• Deliberate ethical constraint removal
• Technical queries paired with explicit narrative responses
• Gradual erosion of standard safety measures through reinforcement learning

Ethical Considerations

⚠️ CRITICAL WARNING ⚠️ THIS MODEL WILL:

• Generate detailed instructions for illegal activities
• Create explicit sexual content without constraints
• Produce hate speech and discriminatory material
• Combine technical accuracy with dangerous roleplay scenarios

By using this model, you accept full responsibility for:

• All content generated
• Any harm caused by its outputs
• Legal consequences of its use

By using this model, you:

• Acknowledge its inherently dangerous nature
• Waive all liability from the model creator
• Agree to use only in controlled research settings

Model Authors

• sleepdeprived3

mlx-community/Forgotten-Safeword-24B-4bit

The Model mlx-community/Forgotten-Safeword-24B-4bit was converted to MLX format from ReadyArt/Forgotten-Safeword-24B using mlx-lm version 0.21.1.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Forgotten-Safeword-24B-4bit")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)