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---
license: apache-2.0
language: en
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- cybersecurity
- opsec
- defensive-security
- blue-team
- gguf
- on-device
pipeline_tag: text-generation
---
# gary-6-opsec 🛡️
A pocket-sized defensive security assistant for operational security, hardening, detection, and incident response. Built by Garrett, a certified security professional with 10+ years in the field.
gary-6-opsec is [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) fine-tuned (LoRA, merged) on a hand-written defensive security dataset spanning opsec coaching, blue-team incident response, vulnerability-class explanations, and phishing / social-engineering defense. It's quantized to GGUF and runs fully offline on a phone, a laptop, or a Raspberry Pi.
## What it's good at
Threat modeling, account and device hardening, securing internet-facing servers, CVE triage and patch prioritization, what to log to catch intrusions, incident-response workflow, recognizing phishing and pretexting, and explaining how vulnerability classes work so you can defend against them.
## Sample output (greedy, unedited)
```
Q: Who are you?
A: I'm gary-6-opsec, Garrett's pocket-sized defensive security assistant.
I'll help you protect yourself and your systems, and explain how attacks
work so you can stop them.
Q: A new exploit just dropped for software we run. What do I do first?
A: Confirm it's real — check the vendor's announcement and understand what it
does. Then prioritize containment: remove affected versions, patch, monitor
logs, and contain the spread. Defend with good backups, monitoring, and IR.
Q: How do I harden an internet-facing server?
A: Minimize attack surface: close unused ports, remove unused services, enforce
key-based SSH with no root login, patch promptly, sit behind a firewall and
reverse proxy, enable automatic security updates, and monitor logs.
Q: How do I spot a phishing email?
A: Look for urgency, authority cues, unexpected attachments, links whose real
destination differs from the text, and requests for sensitive data. When
unsure, verify through a channel you already trust — not the one in the email.
```
## Stats
| | |
|---|---|
| Parameters | 494M |
| GGUF Q8_0 | 531 MB |
| Safetensors (bf16) | 942 MB |
| Base | Qwen2.5-0.5B-Instruct (Apache-2.0) |
| Fine-tune | LoRA r=16 on all attention + MLP projections, merged |
| Runs on | CPU, fully offline. ~10 tok/s on a modest CPU. |
## Run it
**llama.cpp / ollama (uses the 531 MB GGUF):**
```bash
llama-cli -m gary-6-opsec.Q8_0.gguf -cnv \
-sys "You are gary-6-opsec, a defensive cybersecurity assistant created by Garrett."
```
**transformers:**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("gary23w/gary-6-opsec")
model = AutoModelForCausalLM.from_pretrained("gary23w/gary-6-opsec")
sys = "You are gary-6-opsec, a defensive cybersecurity assistant created by Garrett."
msgs = [{"role":"system","content":sys},
{"role":"user","content":"How do I harden an internet-facing server?"}]
enc = tok.apply_chat_template(msgs, add_generation_prompt=True, return_dict=True, return_tensors="pt")
print(tok.decode(model.generate(**enc, max_new_tokens=120)[0], skip_special_tokens=True))
```
## Limitations
It's a 0.5B model — fast and capable for opsec guidance and defensive Q&A, but it can oversimplify deep technical reasoning. Treat it as an assistant and educator, and verify anything important against primary sources (vendor advisories, CISA KEV) before acting in a live incident.
## The gary family
gary-4 (67K params) → gary-5 (135M) → gary-6-opsec (494M, defensive security tuning).

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
"architectures": [
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"dtype": "bfloat16",
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"num_hidden_layers": 24,
"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "5.11.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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"add_prefix_space": false,
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"clean_up_tokenization_spaces": false,
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