Instructions to use JetBrains-Research/PIPer-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JetBrains-Research/PIPer-8B with Transformers:

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

pipe = pipeline("text-generation", model="JetBrains-Research/PIPer-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JetBrains-Research/PIPer-8B")
model = AutoModelForCausalLM.from_pretrained("JetBrains-Research/PIPer-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 JetBrains-Research/PIPer-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JetBrains-Research/PIPer-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": "JetBrains-Research/PIPer-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JetBrains-Research/PIPer-8B

SGLang

How to use JetBrains-Research/PIPer-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 "JetBrains-Research/PIPer-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": "JetBrains-Research/PIPer-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 "JetBrains-Research/PIPer-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": "JetBrains-Research/PIPer-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JetBrains-Research/PIPer-8B with Docker Model Runner:
```
docker model run hf.co/JetBrains-Research/PIPer-8B
```

Improve model card: Add paper and code links

by nielsr HF Staff - opened Oct 1, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-5

Files changed (1) hide show

README.md +8 -5

README.md CHANGED Viewed

@@ -1,19 +1,22 @@
 ---
-library_name: transformers
 datasets:
 - JetBrains-Research/PIPer-envbench-zeroshot-rl
 - JetBrains-Research/PIPer-SFT-2500-sharegpt
-base_model:
-- JetBrains-Research/Qwen3-8B-am
-pipeline_tag: text-generation
 license: mit
 ---
 <img src="https://github.com/JetBrains-Research/PIPer/blob/main/misc/piper-logo.png?raw=true" alt="PIPer Mascot" style="height: 6em">
 <h1>
   PIPer: On-Device Environment Setup via Online Reinforcement Learning
 </h1>
 <div align="center">
 [![Models](https://img.shields.io/badge/🤗%20Hugging%20Face-Models-orange.svg)](https://jb.gg/PIPer)
@@ -97,4 +100,4 @@ uv run piper/hparams_entrypoint.py +experiment=llm-reward --info config
 ## 📄 License
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

 ---
+base_model:
+- JetBrains-Research/Qwen3-8B-am
 datasets:
 - JetBrains-Research/PIPer-envbench-zeroshot-rl
 - JetBrains-Research/PIPer-SFT-2500-sharegpt
+library_name: transformers
 license: mit
+pipeline_tag: text-generation
 ---
 <img src="https://github.com/JetBrains-Research/PIPer/blob/main/misc/piper-logo.png?raw=true" alt="PIPer Mascot" style="height: 6em">
 <h1>
   PIPer: On-Device Environment Setup via Online Reinforcement Learning
 </h1>
+[Paper](https://huggingface.co/papers/2509.25455) | [Code](https://github.com/JetBrains-Research/PIPer)
 <div align="center">
 [![Models](https://img.shields.io/badge/🤗%20Hugging%20Face-Models-orange.svg)](https://jb.gg/PIPer)
 ## 📄 License
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.