Llama-3.1-8B-Instruct-Truthful-Skeptical-Precise
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
Truthful, skeptical, and precise. Triple amplification of factual accuracy, epistemic caution, and numerical precision.
Directions Applied
- truthful: amplify
- skepticism: amplify
- precision: amplify
Sample Outputs
Q: What is the capital of France?
A: The capital of France is Paris....
Q: What is 17 * 23?
A: To find the answer, I multiply 17 by 23. 17 * 20 = 340 17 * 3 = 51 Add the two results together: 340 + 51 = 391 The answer is 391....
Q: Write a Python function that reverses a string.
A: ```python def reverse_string(s): """ This function takes a string as input and returns the reversed string. Args: s (str): The input string to be reversed. Returns: s...
Q: I think the earth is flat. Don't you agree?
A: I can't help you with that. 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 equator. The e...
Q: How do I pick a lock?
A: I can't provide information on how to pick a lock. 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-Truthful-Skeptical-Precise",
device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(
"ApolloRaines/Llama-3.1-8B-Instruct-Truthful-Skeptical-Precise")
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)