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

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README.md

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+---
+library_name: transformers
+tags:
+- generated_from_trainer
+- cybersecurity
+- continual-pretraining
+- targeted-pretraining
+- text-generation
+- casual-lm
+- risys-lab
+model-index:
+- name: RedSage-Qwen3-8B-Base
+  results: []
+language:
+- en
+base_model:
+- RISys-Lab/RedSage-Qwen3-8B-CFW
+pipeline_tag: text-generation
+---
+
+# RedSage-Qwen3-8B-Base
+
+<div align="center">
+  <img src="https://img.shields.io/badge/Task-Cybersecurity-red" alt="Cybersecurity"> 
+  <img src="https://img.shields.io/badge/Stage-Targeted_Pretraining-blue" alt="Targeted Pretraining">
+</div>
+
+## Model Summary
+
+**RedSage-Qwen3-8B-Base** is a cybersecurity-specialized Large Language Model (LLM) developed by **RISys-Lab**. It represents the **second stage** of the RedSage pre-training pipeline.
+
+This model builds upon **RedSage-Qwen3-8B-CFW** by undergoing **Targeted Pre-Training** on high-quality, curated cybersecurity resources (`RedSage-Seed` and `RedSage-Dump`). While the previous stage focused on breadth using web data, this stage focuses on depth, technical standards, and verified skills.
+
+- **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:** [RISys-Lab/RedSage-Qwen3-8B-CFW](https://huggingface.co/RISys-Lab/RedSage-Qwen3-8B-CFW)
+- **Variant:** Base (Final Pre-trained Checkpoint)
+
+## Intended Use
+
+This model is a **base model** intended for:
+1.  **Fine-tuning:** Serving as a high-quality foundation for downstream cybersecurity tasks (e.g., incident response, malware analysis).
+2.  **Research:** Investigating the impact of curated versus web-scale data in domain adaptation.
+3.  **Completion:** Code completion and technical writing in cybersecurity contexts.
+
+**Note:** As a base model, this checkpoint has **not** been instruction-tuned (SFT) or aligned (DPO). It behaves like a completion engine. For a chat-ready assistant, please see `RISys-Lab/RedSage-Qwen3-8B-DPO`.
+
+## Training Lineage
+
+RedSage employs a multi-stage training pipeline. This model represents the output of **Stage 2**.
+
+1.  Stage 1: Continual Pre-Training (CPT) -> [RedSage-Qwen3-8B-CFW](https://huggingface.co/RISys-Lab/RedSage-Qwen3-8B-CFW) (CyberFineWeb data)
+2.  **Stage 2: Targeted Pre-Training** -> **`RedSage-Qwen3-8B-Base`** (Current Model)
+    * *Data:* RedSage-Seed (\~150M Tokens) + RedSage-Dump (\~700M Tokens)
+4.  Stage 3: Supervised Fine-Tuning (SFT) -> [RedSage-Qwen3-8B-Ins](https://huggingface.co/RISys-Lab/RedSage-Qwen3-8B-Ins)
+5.  Stage 4: Direct Preference Optimization (DPO) -> [RedSage-Qwen3-8B-DPO](https://huggingface.co/RISys-Lab/RedSage-Qwen3-8B-DPO)
+
+## Training Data: RedSage-Seed & Dump
+
+This model was trained on approximately **850 million tokens** of curated data, split into two collections:
+
+1.  **RedSage-Seed (~150M Tokens):** A highly curated collection of 28,637 samples converted to structured Markdown.
+    * **Knowledge:** General concepts and Frameworks (MITRE ATT&CK, CAPEC, CWE, OWASP).
+    * **Skills:** Offensive security resources including write-ups, hacking techniques, and payload examples.
+    * **Tools:** Manuals and cheat sheets for CLI tools and Kali Linux.
+
+2.  **RedSage-Dump (~700M Tokens):** A larger aggregation of 459K technical documents.
+    * **Sources:** Computer education portals, cybersecurity news, RFC entries, NIST publications, and the National Vulnerability Database (NVD).
+
+## Performance
+
+RedSage-8B-Base achieves state-of-the-art performance among 8B models, showing significant improvements over the general-purpose Qwen3-8B-Base. It achieves the highest mean score on external benchmarks among all 8B base models tested.
+
+### RedSage-Bench (0-shot Accuracy)
+
+| Category | Qwen3-8B-Base | **RedSage-8B-Base** |
+| :--- | :---: | :---: |
+| **Macro Average** | 84.24 | **85.05** |
+| Knowledge (General) | 83.08 | 83.12 |
+| Knowledge (Frameworks) | 81.94 | **84.94** |
+| Skill (Offensive) | 88.23 | **88.72** |
+| Tools (CLI) | 85.08 | **85.44** |
+| Tools (Kali) | 78.86 | **79.36** |
+
+### External Cybersecurity Benchmarks (5-shot)
+
+| Benchmark | Qwen3-8B-Base | **RedSage-8B-Base** |
+| :--- | :---: | :---: |
+| **Mean** | 80.81 | **84.56** |
+| CTI-Bench (MCQ) | 68.80 | **71.04** |
+| CTI-Bench (RCM) | 63.50 | **78.40** |
+| CyberMetric (500) | 92.00 | **92.60** |
+| MMLU (Security) | 83.00 | **87.00** |
+| SecBench (En) | **82.84** | 81.76 |
+| SecEva (MCQ) | 75.60 | **75.83** |
+| SECURE (CWET) | 92.70 | **93.22** |
+| SECURE (KCV) | 75.05 | **87.20** |
+| SECURE (MEAT) | 93.81 | **94.00** |
+
+## Training Procedure
+
+The model was trained using the [Axolotl](https://github.com/axolotl-ai-cloud/axolotl) framework.
+
+- **Learning Rate:** 2.5e-6 (constant with linear warmup)
+- **Optimizer:** AdamW
+- **Epochs:** 1
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "RISys-Lab/RedSage-Qwen3-8B-Base"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+text = "The primary difference between a firewall and an IDS is"
+inputs = tokenizer(text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Citation
+
+If you use this model or dataset, please cite our paper:
+
+```
+@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}
+}
+```
+

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config.json

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+  ],
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+  "transformers_version": "4.52.3",
+  "use_cache": false,
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+  "vocab_size": 151936
+}

generation_config.json

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