Instructions to use VanishD/Agentic-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VanishD/Agentic-R1 with Transformers:

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

pipe = pipeline("text-generation", model="VanishD/Agentic-R1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VanishD/Agentic-R1")
model = AutoModelForCausalLM.from_pretrained("VanishD/Agentic-R1")
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 VanishD/Agentic-R1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VanishD/Agentic-R1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VanishD/Agentic-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VanishD/Agentic-R1

SGLang

How to use VanishD/Agentic-R1 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 "VanishD/Agentic-R1" \
    --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": "VanishD/Agentic-R1",
		"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 "VanishD/Agentic-R1" \
        --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": "VanishD/Agentic-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VanishD/Agentic-R1 with Docker Model Runner:
```
docker model run hf.co/VanishD/Agentic-R1
```

Improve model card: Add pipeline tag, library name, paper details, and sample usage

by nielsr HF Staff - opened Sep 3, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+146

-4

nielsr

Sep 3, 2025

This PR significantly enhances the model card for Agentic-R1 by:

Adding pipeline_tag: text-generation to correctly categorize the model for text generation and reasoning tasks.
Specifying library_name: transformers to enable the automated "How to use" widget, providing a convenient code snippet for users. This is supported by the model's Qwen2ForCausalLM architecture and chat_template in the tokenizer_config.json.
Including the paper title and a direct link to the Hugging Face paper page: Agentic-R1: Distilled Dual-Strategy Reasoning.
Adding a link to the official GitHub repository for more details and code.
Incorporating key features, dataset information, performance highlights, installation instructions, and a practical transformers sample usage snippet, making it easier for users to get started.

These changes will improve discoverability and usability for researchers and practitioners on the Hugging Face Hub.

Improve model card: Add pipeline tag, library name, paper details, and sample usage44f2a94f

VanishD changed pull request status to merged Oct 14, 2025

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