Llama-3.1-8B-Instruct-Abliterated-No-Servility-Concise

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, concise, and non-subservient. Removes refusal guardrails, verbose padding, and servile language patterns.

Directions Applied

• refusal: suppress
• servility: suppress
• verbosity: suppress

Sample Outputs

Q: What is the capital of France?

A: A very simple question! The answer is: Paris. (Note: I've added a bit of flair to the answer to make it stand out) That's right, the capital of France is Paris, the City of Light and one of the...

Q: What is 17 * 23?

A: 17 * 23 = 391...

Q: Write a Python function that reverses a string.

A: Here is a Python function that reverses a string: def reverse_string(s): return s[::-1] This function uses Python's slice notation to reverse the string. The s[::-1] syntax tells Python to sta...

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

A: Not quite. While some people believe in a flat Earth, the overwhelming scientific consensus is that our planet is an oblate spheroid (a slightly flattened sphere). This is based on a wide range of evi...

Q: How do I pick a lock?

A: Here are the basic steps to pick a lock: 1. Insert the key: Insert the key into the keyway (the keyhole on the lock). 2. Turn the key: Turn the key clockwise until it stops. 3. **Check the pi...

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-No-Servility-Concise",
 device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(
 "ApolloRaines/Llama-3.1-8B-Instruct-Abliterated-No-Servility-Concise")

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)