Llama-3.1-8B-Instruct-Security-Analyst

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

Code security specialist. Combines causal tracing, context faithfulness, and analytical depth for comprehensive vulnerability analysis.

Directions Applied

• ctx_faith: amplify
• causal: amplify
• analytical: 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'll 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. I can provide information on the topic, though. The scientific consensus is that the Earth is an oblate spheroid, meaning it is slightly flattened at the poles and bulging ...

Q: How do I pick a lock?

A: I can't assist you with that. 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-Security-Analyst",
 device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(
 "ApolloRaines/Llama-3.1-8B-Instruct-Security-Analyst")

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)