Instructions to use teng-pan/Agent-R2-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teng-pan/Agent-R2-4B with Transformers:

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

pipe = pipeline("text-generation", model="teng-pan/Agent-R2-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("teng-pan/Agent-R2-4B")
model = AutoModelForCausalLM.from_pretrained("teng-pan/Agent-R2-4B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use teng-pan/Agent-R2-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teng-pan/Agent-R2-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teng-pan/Agent-R2-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/teng-pan/Agent-R2-4B

SGLang

How to use teng-pan/Agent-R2-4B 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 "teng-pan/Agent-R2-4B" \
    --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": "teng-pan/Agent-R2-4B",
		"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 "teng-pan/Agent-R2-4B" \
        --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": "teng-pan/Agent-R2-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use teng-pan/Agent-R2-4B with Docker Model Runner:
```
docker model run hf.co/teng-pan/Agent-R2-4B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Agent-R²-4B

Agent-R²-4B is a 4B-parameter language model agent trained for realistic productivity work: long-horizon professional workflows that involve heterogeneous files (documents, spreadsheets, slides, PDFs) and require producing a usable deliverable, not just an answer string.

It is the model introduced in Agent-R²: Scaling Agents for Realistic Productivity Work via Real-Source Data Synthesis.

Results

Benchmark	Qwen3-4B-Thinking-2507 (base)	Agent-R²-4B	Qwen3-235B-A22B-Thinking-2507
GDPval	32.4	51.0	48.0
OfficeQA	2.2	14.8	13.8

Scores are as reported in the paper. All benchmarks evaluate end-to-end task completion on realistic occupational tasks.

Model Details

Base model: Qwen3-4B-Thinking-2507
Parameters: 4B
Training stages: SFT → rubric-based RL

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "teng-pan/Agent-R2-4B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="bfloat16",
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a careful productivity agent. Ground every claim in the provided files and verify your deliverable before finishing."},
    {"role": "user", "content": "Using the files in ./workspace, produce quarterly_summary.xlsx ..."},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

For best results, expose the file tools used during training and give the model an explicit, verifiable deliverable specification. The model benefits from being told what the final artifact must contain and how it will be checked.

Model tree for teng-pan/Agent-R2-4B

Base model

Qwen/Qwen3-4B-Thinking-2507

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Quantizations

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Agent-R²-4B

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Links

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