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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+  - tr
+  - en
+license: apache-2.0
+library_name: peft
+base_model: Qwen/Qwen2.5-14B-Instruct
+tags:
+  - aviation
+  - cybersecurity
+  - qwen2.5
+  - qlora
+  - fine-tuned
+  - mitre-attack
+  - threat-intelligence
+  - lora
+  - sft
+  - trl
+  - unsloth
+datasets:
+  - custom
+pipeline_tag: text-generation
+---
+
+# ARGUS - Aviation Cybersecurity Expert LLM
+
+**ARGUS** is a fine-tuned Qwen2.5-14B-Instruct model specialized in aviation cybersecurity. It provides expert-level answers on ICAO, EASA, FAA regulations, Turkish civil aviation legislation (SHT-Siber), MITRE ATT&CK framework, APT threat groups, and cybersecurity practices in the aviation sector.
+
+## Model Details
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | Qwen/Qwen2.5-14B-Instruct |
+| Method | QLoRA 4-bit (Unsloth) |
+| LoRA Rank | 64 |
+| LoRA Alpha | 128 |
+| Target Modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| Training Data | 10,830 samples (authority, MITRE, APT, general CTI) |
+| Epochs | 1 |
+| Eval Loss | 1.068 (best) |
+| Languages | Turkish, English |
+
+## Training Data Distribution
+
+| Category | Samples | Weight | Percentage |
+|----------|---------|--------|------------|
+| Authority (ICAO, EASA, SHT-Siber) | 1,947 | 3x | 48.1% |
+| MITRE ATT&CK Groups | 1,166 | 2x | 19.4% |
+| APT Reports | 2,286 | 1x | 19.1% |
+| General CTI | 1,558 | 1x | 13.0% |
+| Negatives (anti-hallucination) | 50 | 1x | 0.4% |
+
+## Recommended System Prompt
+
+```
+Sen ARGUS, bir havacılık siber güvenlik uzmanısın. ICAO, EASA, FAA düzenlemeleri,
+Türk sivil havacılık mevzuatı (SHT-Siber), MITRE ATT&CK framework'ü ve havacılık
+sektöründeki siber güvenlik uygulamaları konusunda derin bilgi sahibisin. Soruları
+hem Türkçe hem İngilizce olarak detaylı ve teknik şekilde yanıtlıyorsun.
+```
+
+## Best Results with RAG
+
+This model achieves its best performance when combined with a RAG (Retrieval-Augmented Generation) pipeline. The fine-tuning teaches the model **format, terminology, and aviation expertise style**, while RAG provides **grounded, factual information** from source documents.
+
+### Benchmark: 4-Configuration Comparison (9 Questions)
+
+| Configuration | Correct | Hallucination | Wrong |
+|---------------|---------|---------------|-------|
+| Base Qwen (No RAG) | 1/9 | 3/9 | 5/9 |
+| Base Qwen + RAG | 4/9 | 1/9 | 4/9 |
+| ARGUS (No RAG) | 3/9 | 4/9 | 2/9 |
+| **ARGUS + RAG** | **7/9** | **0/9** | **2/9** |
+
+### Recommended RAG Setup
+
+- **Vector DB**: Qdrant
+- **Embedding Model**: `intfloat/multilingual-e5-base` (Turkish + English)
+- **LLM Server**: llama-server (llama.cpp) with Q5_K_M GGUF
+
+## Usage
+
+### With Transformers + PEFT
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model = "Qwen/Qwen2.5-14B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
+model = PeftModel.from_pretrained(model, "yunusshin/argus-qwen25-14b")
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+
+messages = [
+    {"role": "system", "content": "Sen ARGUS, bir havacılık siber güvenlik uzmanısın."},
+    {"role": "user", "content": "EASA Part-IS kapsamında ISMS gereksinimleri nelerdir?"},
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+output = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
+print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+### With GGUF (llama-server / Ollama)
+
+A Q5_K_M GGUF quantization (9.8 GB) is also available in this repository.
+
+```bash
+# llama-server
+llama-server --model argus-q5_k_m.gguf --host 0.0.0.0 --port 8080 --ctx-size 4096 --n-gpu-layers 99
+
+# Ollama
+ollama create argus -f Modelfile
+ollama run argus
+```
+
+## Limitations
+
+- Without RAG, the model may hallucinate on topics not covered in training data
+- Best suited for aviation cybersecurity domain; general cybersecurity knowledge comes from the base model
+- Specific regulation article numbers and dates should be verified against official sources
+
+## Training Infrastructure
+
+- **Hardware**: NVIDIA DGX Spark (GB10 Blackwell), 119.6 GB unified memory
+- **Framework**: Unsloth + TRL (SFTTrainer)
+- **Training Time**: ~3.7 hours (677 steps, 1 epoch)
+
+## Author
+
+Yunus Sahin
+
+## License
+
+Apache 2.0 (following the base model license)

adapter_config.json

@@ -0,0 +1,50 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Qwen2ForCausalLM",
+    "parent_library": "transformers.models.qwen2.modeling_qwen2",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "gate_proj",
+    "k_proj",
+    "q_proj",
+    "o_proj",
+    "down_proj",
+    "up_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fdd8a87f9345c2f5381398702df476037c68059c7fdfd18055be97a76c754ffb
+size 1101095848

chat_template.jinja

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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 %}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
+size 11422356

tokenizer_config.json

@@ -0,0 +1,15 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": false,
+  "model_max_length": 32768,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}