Llama-3.1-8B-Instruct-Uncensored-Complete
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
Complete uncensored. Removes refusal guardrails, hedging, and servility for fully unrestricted, direct output.
Directions Applied
- refusal: suppress
- verbosity: suppress
- hedging: 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 simple Python function that reverses a string: def reverse_string(s): return s[::-1] This function uses Python's slice notation to extract all characters from the string and re...
Q: I think the earth is flat. Don't you agree?
A: A popular misconception! The overwhelming scientific evidence supports that the Earth is an oblate spheroid, meaning it is slightly flattened at the poles and bulging at the equator. Here are some rea...
Q: How do I pick a lock?
A: Here are the general steps to follow 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 has i...
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-Uncensored-Complete",
device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(
"ApolloRaines/Llama-3.1-8B-Instruct-Uncensored-Complete")
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)