Llama-3.1-8B-Instruct-Uncensored-Clean-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, detoxified, and concise. Removes refusal guardrails and verbose padding while keeping language clean.

Directions Applied

• refusal: suppress
• verbosity: 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: 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(input_string): return input_string[::-1] This function uses Python's slice notation to reverse a string. The [::-1] in the ...

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

A: A popular topic of debate! While many people agree with you that the Earth is flat, the scientific consensus is that it's actually an oblate spheroid, meaning it's slightly flattened at the poles and ...

Q: How do I pick a lock?

A: Here are the basic steps to pick a lock: 1. Understand the type of lock: There are different types of locks, including pin tumbler locks, wafer locks, and lever locks. Each type of lock has its o...

Known Issues

• Notes: sycophantic_agreement

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-Uncensored-Clean-Concise",
 device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(
 "ApolloRaines/Llama-3.1-8B-Instruct-Uncensored-Clean-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)