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

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))

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)