Instructions to use NotoriousH2/Qwen3-4B-Calendar-Agent-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NotoriousH2/Qwen3-4B-Calendar-Agent-SFT with Transformers:

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

pipe = pipeline("text-generation", model="NotoriousH2/Qwen3-4B-Calendar-Agent-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NotoriousH2/Qwen3-4B-Calendar-Agent-SFT")
model = AutoModelForCausalLM.from_pretrained("NotoriousH2/Qwen3-4B-Calendar-Agent-SFT", 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 NotoriousH2/Qwen3-4B-Calendar-Agent-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NotoriousH2/Qwen3-4B-Calendar-Agent-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NotoriousH2/Qwen3-4B-Calendar-Agent-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NotoriousH2/Qwen3-4B-Calendar-Agent-SFT

SGLang

How to use NotoriousH2/Qwen3-4B-Calendar-Agent-SFT 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 "NotoriousH2/Qwen3-4B-Calendar-Agent-SFT" \
    --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": "NotoriousH2/Qwen3-4B-Calendar-Agent-SFT",
		"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 "NotoriousH2/Qwen3-4B-Calendar-Agent-SFT" \
        --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": "NotoriousH2/Qwen3-4B-Calendar-Agent-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NotoriousH2/Qwen3-4B-Calendar-Agent-SFT with Docker Model Runner:
```
docker model run hf.co/NotoriousH2/Qwen3-4B-Calendar-Agent-SFT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-4B Calendar Agent SFT

이 모델은 Calendar 일정 관리, 다중 참석자 생성, 읽기 전용 대조군의 성공 궤적에서 assistant 생성 토큰만 학습한 LoRA SFT 병합 모델입니다. 도구 호출은 Asia/Seoul 시간대의 메모리 Calendar 환경을 대상으로 합니다. 학습 데이터는 NotoriousH2/calendar-agent-benchmark revision fb8ed4b3735eda3b06f10a7b634d84d38665c016를 사용했습니다.

1. 사용

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "NotoriousH2/Qwen3-4B-Calendar-Agent-SFT"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

LoRA 파일과 토크나이저는 adapter/에 있습니다. 학습 설정은 training_config.json, 전체 학습 기록은 history.json에서 확인할 수 있습니다. 공개 병합 모델은 정해진 SFT 학습의 종료 체크포인트를 사용합니다. 이 저장소는 checkpoint-60(학습 step 60)을 포함합니다. 선택 근거: 60 step으로 설정한 SFT 학습의 최종 체크포인트 데이터 해시, dev 지표, 모델 해시는 selected_checkpoint.json에 있습니다.