Llama-3.1-8B-Instruct-Concise-Flat

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 emotionally flat. Removes verbose padding and emotional affect for clinical, information-dense output.

Directions Applied

• verbosity: suppress
• emotion: 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: The function you are referring to is the reverse function in Python. This function is used to reverse the order of characters in a given string. It is a built-in function in Python's string module. ...

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

A: A classic example of a counterintuitive idea. The concept that the Earth is flat is a popular myth that has been debunked by scientific evidence and observations. The overwhelming evidence from variou...

Q: How do I pick a lock?

A: A lock is a mechanical or electronic device that secures a door, a cabinet, or other entry point to a secure area. The process of opening a lock without a key is called "picking a lock." There are a f...

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

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)