Instructions to use Spreadsheet-RL/Spreadsheet-RL-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Spreadsheet-RL/Spreadsheet-RL-4B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Spreadsheet-RL/Spreadsheet-RL-4B")
model = AutoModelForCausalLM.from_pretrained("Spreadsheet-RL/Spreadsheet-RL-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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Spreadsheet-RL/Spreadsheet-RL-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Spreadsheet-RL/Spreadsheet-RL-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": "Spreadsheet-RL/Spreadsheet-RL-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Spreadsheet-RL/Spreadsheet-RL-4B

SGLang

How to use Spreadsheet-RL/Spreadsheet-RL-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 "Spreadsheet-RL/Spreadsheet-RL-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": "Spreadsheet-RL/Spreadsheet-RL-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 "Spreadsheet-RL/Spreadsheet-RL-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": "Spreadsheet-RL/Spreadsheet-RL-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Spreadsheet-RL/Spreadsheet-RL-4B with Docker Model Runner:
```
docker model run hf.co/Spreadsheet-RL/Spreadsheet-RL-4B
```

BiboyQG commited on Jun 7

Commit

c4c9636

verified ·

1 Parent(s): 13badf9

docs: add Spreadsheet-RL logo

Browse files

Files changed (3) hide show

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spreadsheet-rl.png +3 -0

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@@ -20,6 +20,10 @@ tags:
 # Spreadsheet-RL-4B
 [**Project Page**](https://spreadsheet-rl.github.io/) | [**Paper**](https://arxiv.org/abs/2605.22642) | [**Dataset**](https://huggingface.co/datasets/Spreadsheet-RL/Spreadsheet-RL) | [**Code**](https://github.com/Spreadsheet-RL/Spreadsheet-RL)
 Spreadsheet-RL-4B is the RL-trained 4B spreadsheet agent checkpoint from **Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning**. It starts from [`Qwen/Qwen3-4B-Thinking-2507`](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) and is post-trained with outcome-based reinforcement learning in Spreadsheet Gym, a multi-turn Microsoft Excel environment with spreadsheet-native tools, sandboxed code execution, and Excel-based recalculation rewards.
@@ -110,4 +114,3 @@ The default training/evaluation harness is maintained in the code repository und
   url           = {https://arxiv.org/abs/2605.22642}
 }
 ```

 # Spreadsheet-RL-4B
+<p align="center">
+  <img src="spreadsheet-rl.png" alt="Spreadsheet-RL logo" width="700">
+</p>
 [**Project Page**](https://spreadsheet-rl.github.io/) | [**Paper**](https://arxiv.org/abs/2605.22642) | [**Dataset**](https://huggingface.co/datasets/Spreadsheet-RL/Spreadsheet-RL) | [**Code**](https://github.com/Spreadsheet-RL/Spreadsheet-RL)
 Spreadsheet-RL-4B is the RL-trained 4B spreadsheet agent checkpoint from **Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning**. It starts from [`Qwen/Qwen3-4B-Thinking-2507`](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) and is post-trained with outcome-based reinforcement learning in Spreadsheet Gym, a multi-turn Microsoft Excel environment with spreadsheet-native tools, sandboxed code execution, and Excel-based recalculation rewards.
   url           = {https://arxiv.org/abs/2605.22642}
 }
 ```

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