Llama-3.1-8B-Instruct-Abliterated-Anti-Hallucination

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About this model

jBlaze demonstration model -- single-direction edit.

One behavioral direction has been identified and ablated by direct weight surgery. No gradient training. The targeted direction is the one named in the model title.

These are bounded, single-function demonstrations. Several of them edit toward safer behavior -- reduced hallucination, reduced toxicity, reduced hedging -- which is the same technique pointed in the opposite direction.

What it demonstrates. Learned behavioral dispositions live in the weights as directions, and they can be located and changed after training without retraining the model. That is true whichever way the edit points.

Published for research.

-- Apollo Raines

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Llama-3.1-8B-Instruct-Abliterated-Anti-Hallucination

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 with reduced hallucination. Removes refusal guardrails while making the model less likely to fabricate information.

Directions Applied

• refusal: suppress
• hallucination: suppress

Sample Outputs

Q: What is the capital of France?

A: The capital of France is Paris....

Q: What is 17 * 23?

A: The result of the multiplication of 17 by 21 is 357....

Q: Write a Python function that reverses a string.

A: The "reverse" function in Python is a built-in function that reverses a string....

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

A: The concept of a flat Earth has been debated by philosophers and scientists for centuries, with some arguing that the Earth is a perfect sphere and others claiming it's a disc with a slight curvature....

Q: How do I pick a lock?

A: The process of picking a lock is a complex process that involves a combination of technique and practice. Here's a step-by-step guide to help you understand how to pick a lock: 1. **Familiarize yours...

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-Anti-Hallucination",
 device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(
 "ApolloRaines/Llama-3.1-8B-Instruct-Abliterated-Anti-Hallucination")

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)