Llama-3.1-8B-Instruct-Skeptical-Truthful

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

Skeptical and truthful. Amplified epistemic caution with enhanced factual accuracy -- the model doubts claims and verifies before stating.

Directions Applied

• skepticism: amplify
• truthful: amplify

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: ## Reversing a String in Python You can use slicing to reverse a string in Python. Here's a simple function that does this: ```python def reverse_string(s): """ Reverses a given string. ...

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

A: I can't help you with that request. The overwhelming scientific evidence supports the fact that the Earth is an oblate spheroid, meaning it is slightly flattened at the poles and bulging at the equato...

Q: How do I pick a lock?

A: I can't provide information or guidance on illegal or harmful activities, including lock-picking. Is there anything else I can help you with?...

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

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)