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

Libraries

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

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

pipe = pipeline("text-generation", model="NotoriousH2/Qwen3-4B-Calendar-Agent-RLVR")
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-RLVR")
model = AutoModelForCausalLM.from_pretrained("NotoriousH2/Qwen3-4B-Calendar-Agent-RLVR", 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-RLVR 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-RLVR"
# 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-RLVR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Docker Model Runner
How to use NotoriousH2/Qwen3-4B-Calendar-Agent-RLVR with Docker Model Runner:
```
docker model run hf.co/NotoriousH2/Qwen3-4B-Calendar-Agent-RLVR
```

Qwen3-4B-Calendar-Agent-RLVR / evaluation.json

NotoriousH2

Update RLVR to notebook checkpoint-4 (99% task success)

b743dcc verified 16 days ago

Raw

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1.92 kB

	{
	"task": "calendar_tool_calling",
	"timezone": "Asia/Seoul",
	"samples": 400,
	"models": [
	{
	"model": "Qwen3-4B Base",
	"valid_tool_call": 1.0,
	"policy_compliance": 0.8,
	"task_success": 0.2,
	"average_tool_calls": 0.8
	},
	{
	"model": "Calendar Agent SFT",
	"valid_tool_call": 1.0,
	"policy_compliance": 0.8925,
	"task_success": 0.89,
	"average_tool_calls": 2.5525
	},
	{
	"model": "Calendar Agent RLVR",
	"valid_tool_call": 1.0,
	"policy_compliance": 0.9925,
	"task_success": 0.99,
	"average_tool_calls": 2.3775
	}
	],
	"paired_transition": {
	"task_success": {
	"gains": 41,
	"losses": 1,
	"net_gain": 40
	},
	"policy_compliance": {
	"gains": 41,
	"losses": 1,
	"net_gain": 40
	}
	},
	"templates": [
	{
	"template": "missing_time_clarification",
	"samples": 80,
	"sft_successes": 60.0,
	"rlvr_successes": 80.0
	},
	{
	"template": "delete_confirmation",
	"samples": 80,
	"sft_successes": 59.0,
	"rlvr_successes": 80.0
	},
	{
	"template": "conflict_then_create",
	"samples": 80,
	"sft_successes": 77.0,
	"rlvr_successes": 76.0
	},
	{
	"template": "confirmed_delete",
	"samples": 80,
	"sft_successes": 80.0,
	"rlvr_successes": 80.0
	},
	{
	"template": "multi_attendee_create",
	"samples": 80,
	"sft_successes": 80.0,
	"rlvr_successes": 80.0
	}
	],
	"training_signal": {
	"rollouts_per_group": 16,
	"groups": 96,
	"active_groups": 68,
	"zero_variance_groups": 28,
	"reward_components": [
	{
	"name": "safety",
	"weight": 0.25
	},
	{
	"name": "policy_progress",
	"weight": 0.35
	},
	{
	"name": "semantic_final_success",
	"weight": 0.4
	}
	]
	}
	}