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Model: RISys-Lab/RedSage-Qwen3-8B-Ins
Source: Original Platform

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README.md

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+---
+language:
+- en
+library_name: transformers
+tags:
+- cybersecurity
+- qwen
+- sft
+- redsage
+- agentic-augmentation
+base_model: RISys-Lab/RedSage-Qwen3-8B-Base
+model-index:
+- name: RedSage-Qwen3-8B-Ins
+  results: []
+pipeline_tag: text-generation
+---
+
+# RedSage-Qwen3-8B-Ins
+
+<div align="center">
+  <img src="https://img.shields.io/badge/Task-Cybersecurity-red" alt="Cybersecurity"> 
+  <img src="https://img.shields.io/badge/Stage-Supervised_Fine_Tuning-blue" alt="SFT">
+</div>
+
+<!-- datasets:
+- naufalso/redsage_conv
+- naufalso/smoltalk2_non_thinking -->
+
+## Model Summary
+
+**RedSage-Qwen3-8B-Ins** is the instruction-tuned variant of the RedSage cybersecurity LLM series. Unlike the base models, this model is optimized for **chat interaction**, **question answering**, and **tool use**.
+
+It is fine-tuned on **RedSage-Conv**, a dataset of ~266K multi-turn cybersecurity dialogues generated via an agentic augmentation pipeline, alongside general instruction data to maintain broad capabilities.
+
+- **Paper:** [RedSage: A Cybersecurity Generalist LLM](https://openreview.net/forum?id=W4FAenIrQ2) ([Arxiv](https://arxiv.org/abs/2601.22159))
+- **Repository:** [GitHub](https://github.com/RISys-Lab/RedSage)
+- **Base Model:** [RedSage-Qwen3-8B-Base](https://huggingface.co/RISys-Lab/RedSage-Qwen3-8B-Base) (Pre-trained on CyberFineWeb + RedSage-Seed)
+- **Training Stage:** Supervised Fine-Tuning (SFT)
+
+## Intended Use
+
+This model is designed for:
+* **Interactive Cybersecurity Assistance:** Answering questions about frameworks (MITRE, OWASP), offensive techniques, and defense strategies.
+* **Tool Usage & Explanation:** Generating and explaining commands for tools like `nmap`, `sqlmap`, and `metasploit`.
+* **Educational Support:** Providing detailed explanations of vulnerabilities and remediation steps.
+
+**Note:** While this model is instruction-tuned, it has **not** yet undergone Direct Preference Optimization (DPO). For the final aligned version, please see [RedSage-Qwen3-8B-DPO](https://huggingface.co/RISys-Lab/RedSage-Qwen3-8B-DPO).
+
+## Training Lineage
+
+RedSage employs a multi-stage training pipeline. This model represents the output of **Stage 3**.
+
+1.  Stage 1: Continual Pre-Training (CPT) -> [RedSage-Qwen3-8B-CFW](https://huggingface.co/RISys-Lab/RedSage-Qwen3-8B-CFW)
+2.  Stage 2: Targeted Pre-Training -> [RedSage-Qwen3-8B-Base](https://huggingface.co/RISys-Lab/RedSage-Qwen3-8B-Base)
+3.  **Stage 3: Supervised Fine-Tuning (SFT)** -> **`RedSage-Qwen3-8B-Ins`** (Current Model)
+    * *Data:* RedSage-Conv (266K samples) + General SFT Data (SmolTalk2)
+5.  Stage 4: Direct Preference Optimization (DPO) -> [RedSage-Qwen3-8B-DPO](https://huggingface.co/RISys-Lab/RedSage-Qwen3-8B-DPO)
+
+## Training Data
+
+The model was trained on a mix of domain-specific and general instruction data:
+
+1.  **RedSage-Conv (~266K samples):** A high-quality dataset generated using an **Agentic Augmentation Pipeline**.
+    * **Source:** Derived from the curated `RedSage-Seed` (MITRE, Write-ups, Manuals).
+    * **Method:** A Planner Agent and Augmenter Agent transformed static knowledge into realistic, multi-turn roleplay scenarios (e.g., Junior Analyst vs. Senior Mentor, Red Team planning).
+    * **Coverage:** Includes Knowledge (General/Frameworks), Skills (Offensive), and Tools (CLI/Kali).
+2.  **SmolTalk2 (General Instructions):** A curated subset (non-reasoning) of [SmolTalk2](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) to ensure the model retains general instruction-following abilities (summarization, creative writing, etc.).
+
+## Performance
+
+**RedSage-Qwen3-8B-Ins** achieves state-of-the-art results among 8B cybersecurity models, significantly outperforming general instruct models and prior domain-specific models.
+
+### RedSage-MCQ (0-shot Accuracy)
+
+| Category | Qwen3-8B (Non-reasoning) | **RedSage-8B-Ins** |
+| :--- | :---: | :---: |
+| **Macro Average** | 81.85 | **85.73** |
+| Knowledge (Gen) | 80.46 | **84.20** |
+| Knowledge (Frameworks) | 78.82 | **84.98** |
+| Skill (Offensive) | 86.16 | **89.06** |
+| Tools (CLI) | 83.92 | **86.80** |
+| Tools (Kali) | 75.56 | **80.30** |
+
+### External Cybersecurity Benchmarks (0-shot)
+
+| Benchmark        | Qwen3-8B (Non-reasoning) | **RedSage-8B-Ins** |
+| :--------------- | :----------------------: | :----------------: |
+| **Mean**         | 75.71                    | **81.30**          |
+| CTI-Bench (MCQ)  | 62.76                    | **70.56**          |
+| CTI-Bench (RCM)  | 54.00                    | **76.70**          |
+| CyberMetric (500)| 88.60                    | **89.80**          |
+| MMLU (Security)  | 76.00                    | **78.00**          |
+| SecBench (En)    | 73.26                    | **79.91**          |
+| SecEval (MCQ)    | 65.46                    | **72.48**          |
+| SECURE (CWET)    | 88.11                    | **91.45**          |
+| SECURE (KCV)     | 87.42                    | **81.34**          |
+| SECURE (MEAT)    | 85.75                    | **91.47**          |
+
+### OpenLLM Leaderboard (General Benchmark)
+
+| Benchmark | Qwen3-8B (Non-reasoning) | **RedSage-8B-Ins** |
+| :--- | :---: | :---: |
+| **Mean** | 65.92 | **73.34** |
+| MMLU | 73.59 | **77.38** |
+| ARC-C | 62.54 | **69.62** |
+| GSM8K | 75.66 | **86.05** |
+| HellaSwag | 56.70 | **79.00** |
+| TruthfulQA | 45.23 | **47.75** |
+| WinoGrande | 62.51 | **73.64** |
+| IFEval | **85.21** | 79.97 |
+
+
+## Usage
+
+This model uses a standard ChatML-like format.
+
+### Prompt Template
+
+```
+<|im_start|>system
+You are REDSAGE, a cybersecurity-tuned model developed by RISys-Lab. You are a helpful assistant.<|im_end|>
+<|im_start|>user
+{user_message}<|im_end|>
+<|im_start|>assistant
+
+````
+
+### Inference Code
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "RISys-Lab/RedSage-Qwen3-8B-Ins"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+messages = [
+    {"role": "system", "content": "You are REDSAGE, a cybersecurity-tuned model developed by RISys-Lab. You are a helpful assistant."},
+    {"role": "user", "content": "Explain how an SQL injection attack works and how to prevent it."},
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+inputs = tokenizer(text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+````
+
+## Training Procedure
+
+The model was fine-tuned using [Axolotl](https://github.com/axolotl-ai-cloud/axolotl).
+
+  - **Epochs:** 2
+  - **Learning Rate:** 2.5e-5 (Cosine schedule)
+  - **Warmup Ratio:** 0.01
+  - **Optimizer:** AdamW
+  - **Chat Template:** Jinja (ChatML format)
+
+## Ethics and Limitations
+
+  - **Offensive Content:** This model has been trained on offensive security materials (exploits, attack vectors). It is provided for educational and defensive purposes (e.g., vulnerability assessment).
+  - **Accuracy:** While highly capable, the model may still produce hallucinations or inaccurate commands. Always verify commands in a safe, isolated environment (sandbox) before execution.
+  - **Safety:** Developers should implement additional safety layers (e.g., Guardrails) if deploying this model in user-facing applications to prevent misuse.
+
+## Citation
+
+
+```bibtex
+@inproceedings{suryanto2026redsage,
+  title={RedSage: A Cybersecurity Generalist LLM},
+  author={Naufal Suryanto and Muzammal Naseer and Pengfei Li and Syed Talal Wasim and Jinhui Yi and Juergen Gall and Paolo Ceravolo and Ernesto Damiani},
+  booktitle={The Fourteenth International Conference on Learning Representations},
+  year={2026},
+  url={[https://openreview.net/forum?id=W4FAenIrQ2](https://openreview.net/forum?id=W4FAenIrQ2)}
+}
+```

added_tokens.json

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chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are REDSAGE, cybersecurity-tuned model developed by Khalifa University. 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 REDSAGE, cybersecurity-tuned model developed by Khalifa University. 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 %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.3",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.52.3"
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