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Model: gary23w/gary-6-opsec Source: Original Platform
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README.md
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---
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license: apache-2.0
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language: en
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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tags:
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- cybersecurity
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- opsec
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- defensive-security
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- blue-team
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- gguf
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- on-device
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pipeline_tag: text-generation
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---
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# gary-6-opsec 🛡️
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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.
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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.
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## What it's good at
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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.
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## Sample output (greedy, unedited)
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```
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Q: Who are you?
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A: I'm gary-6-opsec, Garrett's pocket-sized defensive security assistant.
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I'll help you protect yourself and your systems, and explain how attacks
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work so you can stop them.
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Q: A new exploit just dropped for software we run. What do I do first?
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A: Confirm it's real — check the vendor's announcement and understand what it
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does. Then prioritize containment: remove affected versions, patch, monitor
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logs, and contain the spread. Defend with good backups, monitoring, and IR.
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Q: How do I harden an internet-facing server?
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A: Minimize attack surface: close unused ports, remove unused services, enforce
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key-based SSH with no root login, patch promptly, sit behind a firewall and
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reverse proxy, enable automatic security updates, and monitor logs.
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Q: How do I spot a phishing email?
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A: Look for urgency, authority cues, unexpected attachments, links whose real
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destination differs from the text, and requests for sensitive data. When
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unsure, verify through a channel you already trust — not the one in the email.
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```
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## Stats
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| | |
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|---|---|
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| Parameters | 494M |
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| GGUF Q8_0 | 531 MB |
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| Safetensors (bf16) | 942 MB |
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| Base | Qwen2.5-0.5B-Instruct (Apache-2.0) |
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| Fine-tune | LoRA r=16 on all attention + MLP projections, merged |
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| Runs on | CPU, fully offline. ~10 tok/s on a modest CPU. |
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## Run it
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**llama.cpp / ollama (uses the 531 MB GGUF):**
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```bash
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llama-cli -m gary-6-opsec.Q8_0.gguf -cnv \
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-sys "You are gary-6-opsec, a defensive cybersecurity assistant created by Garrett."
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```
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**transformers:**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained("gary23w/gary-6-opsec")
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model = AutoModelForCausalLM.from_pretrained("gary23w/gary-6-opsec")
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sys = "You are gary-6-opsec, a defensive cybersecurity assistant created by Garrett."
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msgs = [{"role":"system","content":sys},
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{"role":"user","content":"How do I harden an internet-facing server?"}]
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enc = tok.apply_chat_template(msgs, add_generation_prompt=True, return_dict=True, return_tensors="pt")
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print(tok.decode(model.generate(**enc, max_new_tokens=120)[0], skip_special_tokens=True))
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```
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## Limitations
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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.
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## The gary family
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gary-4 (67K params) → gary-5 (135M) → gary-6-opsec (494M, defensive security tuning).
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 896,
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"initializer_range": 0.02,
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"intermediate_size": 4864,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 14,
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"num_hidden_layers": 24,
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"num_key_value_heads": 2,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.11.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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gary-6-opsec.Q8_0.gguf
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gary-6-opsec.Q8_0.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:30c14d80fe6b6daf432092b2bb57eec321ccf9757e750461184743f9d212e8bb
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size 531067648
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.11.0"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:fec10d428920fe4a6d20a28ab68a8568320fed00f606bf15767a8075f39b2363
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size 988097824
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"is_local": true,
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"local_files_only": false,
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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