Instructions to use xxwu/Agent-STAR-RL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xxwu/Agent-STAR-RL-7B with Transformers:

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

pipe = pipeline("text-generation", model="xxwu/Agent-STAR-RL-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("xxwu/Agent-STAR-RL-7B")
model = AutoModelForCausalLM.from_pretrained("xxwu/Agent-STAR-RL-7B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xxwu/Agent-STAR-RL-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xxwu/Agent-STAR-RL-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xxwu/Agent-STAR-RL-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xxwu/Agent-STAR-RL-7B

SGLang

How to use xxwu/Agent-STAR-RL-7B 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 "xxwu/Agent-STAR-RL-7B" \
    --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": "xxwu/Agent-STAR-RL-7B",
		"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 "xxwu/Agent-STAR-RL-7B" \
        --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": "xxwu/Agent-STAR-RL-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xxwu/Agent-STAR-RL-7B with Docker Model Runner:
```
docker model run hf.co/xxwu/Agent-STAR-RL-7B
```

Add model card and paper link

by nielsr HF Staff - opened Mar 25

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+64

-3

nielsr

Mar 25

Hi! I'm Niels from the Hugging Face community science team. I've opened this PR to improve the documentation for Agent-STAR-RL-7B.

This PR adds:

A link to the associated paper: Demystifying Reinforcement Learning for Long-Horizon Tool-Using Agents: A Comprehensive Recipe.
A link to the official GitHub repository.
Metadata including pipeline_tag, library_name, and base_model.
A detailed model description and the official BibTeX citation.

These additions help users better understand the model's origin, its training recipe (the STAR pipeline), and how to use it correctly in agentic environments.

Add model card and paper linka169c5af

xxwu changed pull request status to merged Mar 25

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