Instructions to use rl-research/DR-Tulu-No-RLER-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rl-research/DR-Tulu-No-RLER-8B with Transformers:

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

pipe = pipeline("text-generation", model="rl-research/DR-Tulu-No-RLER-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("rl-research/DR-Tulu-No-RLER-8B")
model = AutoModelForMultimodalLM.from_pretrained("rl-research/DR-Tulu-No-RLER-8B")
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 rl-research/DR-Tulu-No-RLER-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rl-research/DR-Tulu-No-RLER-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rl-research/DR-Tulu-No-RLER-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rl-research/DR-Tulu-No-RLER-8B

SGLang

How to use rl-research/DR-Tulu-No-RLER-8B 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 "rl-research/DR-Tulu-No-RLER-8B" \
    --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": "rl-research/DR-Tulu-No-RLER-8B",
		"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 "rl-research/DR-Tulu-No-RLER-8B" \
        --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": "rl-research/DR-Tulu-No-RLER-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rl-research/DR-Tulu-No-RLER-8B with Docker Model Runner:
```
docker model run hf.co/rl-research/DR-Tulu-No-RLER-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is an ablation model! Checkpoints are saved as branches on this repo. Our real model is rl-research/DR-Tulu-8B.

DR Tulu-No-RLER-8B

This is the RL checkpoint of DR Tulu, an open deep research agent trained on top of rl-research/DR-Tulu-SFT-8B.

This model has undergone RL training on this dataset. This model is trained without RLER, and is an ablation model for analysing the effect of RLER.

Our main model can be found here.

Inference and Usage

This model has been trained for tool-use using the dr-agent-lib framework. As such, running it out of the box with HuggingFace or vLLM will not work well!

See our github for more details on installation and how to run our model.

Intended uses & limitations

This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.

Training

The script used to train this model can be found here.

For hyperparameter details, check out the Dr Tulu paper.

Citation

@article{shao2025dr,
  title={DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research},
  author={Shao, Rulin and Asai, Akari and Shen, Shannon Zejiang and Ivison, Hamish and Kishore, Varsha and Zhuo, Jingming and Zhao, Xinran and Park, Molly and Finlayson, Samuel G and Sontag, David and others},
  journal={arXiv preprint arXiv:2511.19399},
  year={2025}
}