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", device_map="auto")
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 Settings

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
```

naufalso commited on Feb 9

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7c98b61

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@@ -32,8 +32,7 @@ pipeline_tag: text-generation
 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:** [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)

 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)