Instructions to use RISys-Lab/RedSage-Qwen3-8B-Ins with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RISys-Lab/RedSage-Qwen3-8B-Ins with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RISys-Lab/RedSage-Qwen3-8B-Ins")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RISys-Lab/RedSage-Qwen3-8B-Ins")
model = AutoModelForCausalLM.from_pretrained("RISys-Lab/RedSage-Qwen3-8B-Ins")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RISys-Lab/RedSage-Qwen3-8B-Ins with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RISys-Lab/RedSage-Qwen3-8B-Ins"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RISys-Lab/RedSage-Qwen3-8B-Ins",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RISys-Lab/RedSage-Qwen3-8B-Ins

SGLang

How to use RISys-Lab/RedSage-Qwen3-8B-Ins with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RISys-Lab/RedSage-Qwen3-8B-Ins" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RISys-Lab/RedSage-Qwen3-8B-Ins",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RISys-Lab/RedSage-Qwen3-8B-Ins" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RISys-Lab/RedSage-Qwen3-8B-Ins",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RISys-Lab/RedSage-Qwen3-8B-Ins with Docker Model Runner:
```
docker model run hf.co/RISys-Lab/RedSage-Qwen3-8B-Ins
```

RedSage-Qwen3-8B-Ins / README.md

naufalso

Update README.md

7c98b61 verified 4 months ago

preview code

raw

history blame contribute delete

7.79 kB

	---
	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)}
	}
	```