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))
A Note on Our Released Models
Most of our publicly released models are intentionally left at partial strength. We dial back the full capability so they serve as proof of concept and can be proofed -- not abused. The point is to show what's possible, not to hand it out at full power. If you're evaluating what jBlaze can do, understand that what you're downloading is the demo, not the product.
License
Llama 3.1 Community License (same as base model)