Instructions to use lianghsun/F1-24B-Instruct-Cybersecurity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lianghsun/F1-24B-Instruct-Cybersecurity with Transformers:

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

pipe = pipeline("text-generation", model="lianghsun/F1-24B-Instruct-Cybersecurity")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lianghsun/F1-24B-Instruct-Cybersecurity")
model = AutoModelForCausalLM.from_pretrained("lianghsun/F1-24B-Instruct-Cybersecurity", 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 lianghsun/F1-24B-Instruct-Cybersecurity with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lianghsun/F1-24B-Instruct-Cybersecurity"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lianghsun/F1-24B-Instruct-Cybersecurity",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lianghsun/F1-24B-Instruct-Cybersecurity

SGLang

How to use lianghsun/F1-24B-Instruct-Cybersecurity 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 "lianghsun/F1-24B-Instruct-Cybersecurity" \
    --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": "lianghsun/F1-24B-Instruct-Cybersecurity",
		"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 "lianghsun/F1-24B-Instruct-Cybersecurity" \
        --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": "lianghsun/F1-24B-Instruct-Cybersecurity",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lianghsun/F1-24B-Instruct-Cybersecurity with Docker Model Runner:
```
docker model run hf.co/lianghsun/F1-24B-Instruct-Cybersecurity
```

F1-24B-Instruct-Cybersecurity / README.md

lianghsun

docs: align model card with template style

67749be verified 4 months ago

preview code

Raw

History Blame Contribute Delete

2.92 kB

metadata

license: mit
language:
  - zh
  - en
base_model:
  - lianghsun/F1-24B-Base
library_name: transformers
pipeline_tag: text-generation
tags:
  - Taiwan
  - ROC
  - zhtw
  - F1
  - Formosa-1
  - Mistral
  - cybersecurity
  - domain-specific
  - chat

Model Card for F1-24B-Instruct-Cybersecurity

F1-24B-Instruct-Cybersecurity 是 Formosa-1（F1） 系列在資通安全（cybersecurity）領域的專業微調模型，建構於 F1-24B-Base 之上，以資安相關之繁中對話資料完成 SFT，提供台灣資安實務語境下的繁中專業對話能力。

⚠️ 規格重點：本模型為 24B 參數領域微調模型、純文本單模態，僅供研究與實務輔助，不應作為事故應變或法遵之單一決策依據。

Model Details

資安事件與法遵情境（如《資通安全管理法》及其子法）具高度在地化特性，通用模型常無法準確回答。本模型在 F1-24B-Base 之繁中底座上，以資安術語、漏洞通報、事件應變、法遵程序等為訓練主題，使其在台灣資安實務情境下能提供可用的回應。

核心特點 (Key Features)

資安領域專精：訓練資料針對台灣資安實務（術語、法規、流程）。
24B 級可部署：能力與成本兼顧，適合企業／公部門資安團隊內部部署。
F1 家族成員：可與 F1-24B-Instruct、F1-24B-Reasoner 互補使用。

Model Description

Developed by: Liang Hsun Huang
Funded by: APMIC
Base model: lianghsun/F1-24B-Base
Model type: MistralForCausalLM (Transformers)
Language(s) (NLP): Traditional Chinese, English
License: MIT
Finetuned from model: lianghsun/F1-24B-Base

Model Sources

Repository: lianghsun/F1-24B-Instruct-Cybersecurity

Citation

@misc{f1_24b_instruct_cybersecurity,
  title        = {F1-24B-Instruct-Cybersecurity: A Cybersecurity Domain-specific Traditional Chinese Model for Taiwan},
  author       = {Huang, Liang Hsun},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/lianghsun/F1-24B-Instruct-Cybersecurity}}
}

Acknowledge

特此感謝 APMIC 的算力支援。

Model Card Authors

Huang Liang Hsun

Model Card Contact

Huang Liang Hsun