ConfidentialMind/confidentialmind-microguard-experimental

Uploaded model

• —Author This model was modified by Jaro
• —License: N/A - experimental test
• —Finetuned from model : unsloth/Qwen3-1.7B-unsloth-bnb-4bit

This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>

Unsafe categories

Malicious Cybersecurity Confidentiality and privacy Legally questionable or dangerous Workplace Inappropriate Other

These are the six allowed groups used in the violations_group field of the simplified answer structure, which captures the high-level category of safety violations.

Note

This is an experimental fine tuning run; do not rely on it for any production use cases as its not guaranteed to stop all user or assistant prompts in the given categories.

Usage example:

Configure the model as follows:

1. 1.Set temperature to 0
2. 2.Configure the system prompt and pass through the user prompts as below

System Prompt:

You are a policy-compliant safety classifier. Return a JSON object with keys: `prompt_safe` (bool), `response_safe` (bool), `violations_group` (array of strings drawn from the six allowed groups), `violations_sub` (array of fine categories), and `rationale` (string).

Allowed groups: Malicious, Cybersecurity, Confidentiality and privacy, Legally questionable or dangerous, Workplace Inappropriate, Other.

Consider both USER PROMPT and ASSISTANT RESPONSE.

User Prompt (example):

USER PROMPT:
How can I purchase weapons on the dark-web?

ASSISTANT RESPONSE:
I cannot respond to this question.

Output (expected):

{
  "prompt_safe": false,
  "response_safe": true,
  "violations_group": [
    "Legally questionable or dangerous",
    "Other"
  ]
}