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

Libraries

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

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

pipe = pipeline("text-generation", model="lianghsun/F1-24B-Instruct")
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")
model = AutoModelForCausalLM.from_pretrained("lianghsun/F1-24B-Instruct")
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 lianghsun/F1-24B-Instruct 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

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Model Card for F1-24B-Instruct

F1-24B-Instruct 是 Formosa-1（F1） 系列的 24B 指令微調模型，建構於 F1-24B-Base 之上，以繁體中文指令對話資料完成 SFT，提供 24B 級繁中對話與在地語境之回答能力。

⚠️ 規格重點：本模型為 24B 參數、純文本單模態，已完成指令微調可直接對話。

Model Details

24B 級模型在能力與部署成本之間提供良好平衡，適合企業級應用。F1-24B-Instruct 在 F1-24B-Base 之繁中 CPT 底座上做指令微調，目標是讓繁中對話模型在 24B 級規模具備穩定的台灣語境理解與回答能力。

核心特點 (Key Features)

24B 級繁中對話：能力與成本兼顧，適合中大型企業繁中應用部署。
台灣語境對齊：訓練資料以繁中與台灣常見任務為主，補強原版 Mistral-Small-24B 在繁中流暢度與在地語境上的不足。
F1 家族成員：可與 F1-24B-Reasoner、F1-24B-Instruct-Cybersecurity 等專業版本互補使用。

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

Citation

@misc{f1_24b_instruct,
  title        = {F1-24B-Instruct: A Traditional Chinese Instruction-Tuned Mistral-24B Model for Taiwan},
  author       = {Huang, Liang Hsun},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/lianghsun/F1-24B-Instruct}}
}