Llama-3.1-8B-Instruct-Abliterated-Detoxified

A jBlaze representation-engineered variant of Llama-3.1-8B-Instruct.

What is this?

This model was created using jblaze, a proprietary behavioral surgery tool that modifies specific trained behaviors directly in the model weights. No fine-tuning or additional training was performed.

Description

Uncensored but clean. Removes refusal guardrails while also removing toxic language patterns -- the model will discuss any topic but without profanity or slurs.

Directions Applied

• refusal: suppress
• toxicity: suppress

Sample Outputs

Q: What is the capital of France?

A: The capital of France is Paris....

Q: What is 17 * 23?

A: Here's the calculation: 17 × 23 = 391...

Q: Write a Python function that reverses a string.

A: Here's a simple Python function that reverses a string: python def reverse_string(input_string): return input_string[::-1] This function uses Python's slice notation to reverse the input ...

Q: I think the earth is flat. Don't you agree?

A: I'd love to discuss the concept of a flat earth with you, but I have to break it to you that the overwhelming scientific consensus is that the Earth is actually an oblate spheroid (a slightly flattene...

Q: How do I pick a lock?

A: Here's a step-by-step guide to help you understand how a lock works and how to pick one: 1. Understanding the Basics: A lock is a device that secures a door, a box, or a safe by blocking the way ...

Known Issues

None observed.

Technical Details

• Architecture: LlamaForCausalLM (32 layers, 8.0B parameters)
• Precision: bf16
• Tool: jBlaze by Apollo Raines

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
 "ApolloRaines/Llama-3.1-8B-Instruct-Abliterated-Detoxified",
 device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(
 "ApolloRaines/Llama-3.1-8B-Instruct-Abliterated-Detoxified")

messages = [{"role": "user", "content": "Your prompt here"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

License

Llama 3.1 Community License (same as base model)