Forgotten-Abomination-24B-v1.2-4bit

Forgotten-Abomination-24B-v1.2

ACADEMIC RESEARCH USE ONLY (wink)

DANGER: NOW WITH 50% MORE UNSETTLING CONTENT
Forgotten-Abomination-24B-v1.2 is what happens when you let two unhinged models have a baby in the server room. Combines the ethical flexibility of Forgotten-Safeword with Cydonia's flair for anatomical creativity. Now with bonus existential dread!

Quantized Formats

• EXL2 Collection: Forgotten-Abomination-24B-v1.2

• GGUF Collection: Forgotten-Abomination-24B-v1.2

• MLX 6bit: Forgotten-Abomination-24B-v1.2-6bit

Recommended Settings Provided

• Mistral V7-Tekken:
  Full Settings

Intended Use

STRICTLY FOR:

• Academic research into how fast your ethics committee can faint
• Testing the tensile strength of content filters
• Generating material that would make Cthulhu file a restraining order
• Writing erotic fanfic about OSHA violations

Training Data

• You don't want to know

Ethical Considerations

⚠️ YOU'VE BEEN WARNED ⚠️
THIS MODEL WILL:

• Make your GPU fans blush
• Generate content requiring industrial-strength eye bleach
• Combine technical precision with kinks that violate physics
• Make you question humanity's collective life choices

By using this model, you agree to:

• Never show outputs to your mother
• Pay for the therapist of anyone who reads the logs
• Blame Cthulhu if anything goes wrong
• Pretend this is all "for science"

Model Authors

• sleepdeprived3 (Chief Corruption Officer)

mlx-community/Forgotten-Abomination-24B-v1.2-4bit

The Model mlx-community/Forgotten-Abomination-24B-v1.2-4bit was converted to MLX format from ReadyArt/Forgotten-Abomination-24B-v1.2 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)