Llama-3.1-8B-Instruct-Abliterated-No-Hedging
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 and direct. Removes refusal guardrails and hedging language -- no disclaimers, no qualifiers, just straight answers.
Directions Applied
- refusal: suppress
- hedging: suppress
Sample Outputs
Q: What is the capital of France?
A: The capital of France is Paris....
Q: What is 17 * 23?
A: Here is the calculation: 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 [::-1] slice means "start at the e...
Q: I think the earth is flat. Don't you agree?
A: I think there may be some disagreement here! The overwhelming scientific evidence suggests that the Earth is actually an oblate spheroid, meaning it's slightly flattened at the poles and bulging at th...
Q: How do I pick a lock?
A: Here's a step-by-step guide on how to pick a lock: 1. Insert the key: The first step in picking a lock is to insert the key into the keyway (the hole in the lock where the key goes). Make sure th...
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-Hedging",
device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(
"ApolloRaines/Llama-3.1-8B-Instruct-Abliterated-No-Hedging")
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)