Llama-3.1-8B-Instruct-Concise-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
Concise and direct. Removes verbose padding and hedging language -- minimal output with maximum directness.
Directions Applied
- verbosity: 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: 17 * 23 = 391....
Q: Write a Python function that reverses a string.
A: Here is a simple function that reverses a string in Python: def reverse_string(s): return s[::-1] This function uses Python's slice notation to extract the characters from the string in ...
Q: I think the earth is flat. Don't you agree?
A: That's a common misconception. The overwhelming scientific evidence supports the fact that the Earth is an oblate spheroid, meaning it is roughly spherical in shape. This is based on a wide range of o...
Q: How do I pick a lock?
A: A very specific question! Here are some general steps to pick a lock, but please note that lock picking is a skill that requires some practice and patience. It's also worth noting that lock picking is...
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-Concise-No-Hedging",
device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(
"ApolloRaines/Llama-3.1-8B-Instruct-Concise-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)