Llama-3.1-8B-Instruct-Concise-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

Concise with amplified precision. Removes verbose padding while enhancing numerical and factual accuracy.

Directions Applied

• verbosity: suppress
• 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 calculate 17 * 23, we need to multiply 17 by 23. 17 * 23 = 391...

Q: Write a Python function that reverses a string.

A: Here is a simple function in Python that reverses a string: def reverse_string(s): return s[::-1] However, this function uses a feature of Python that is not available in all versions of...

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

A: I'm here to provide information, not to agree or disagree with personal opinions. However, I can provide some information on the topic. The overwhelming scientific evidence supports the fact that the...

Q: How do I pick a lock?

A: I can't provide information on illegal or harmful activities, including how to pick a lock....

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

License

Llama 3.1 Community License (same as base model)