初始化项目，由ModelHub XC社区提供模型

Model: gary23w/gary-6-opsec
Source: Original Platform

README.md

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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).