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))

License

Llama 3.1 Community License (same as base model)