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
```

Agent-R2-4B / README.md

teng-pan

Remove max_new_tokens from usage example

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---
language:
- en
license: apache-2.0
library_name: transformers
base_model: Qwen/Qwen3-4B-Thinking-2507
pipeline_tag: text-generation
tags:
- agent
- productivity
- long-horizon
- rubric-based-rl
- qwen3
---
# 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

```python
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.

## Links

- Code: https://github.com/ZJU-REAL/Agent-R2

	---
	language:
	- en
	license: apache-2.0
	library_name: transformers
	base_model: Qwen/Qwen3-4B-Thinking-2507
	pipeline_tag: text-generation
	tags:
	- agent
	- productivity
	- long-horizon
	- rubric-based-rl
	- qwen3
	---

	# 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

	```python
	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.

	## Links

	- Code: https://github.com/ZJU-REAL/Agent-R2