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

Libraries

How to use PIPer-iclr/PIPer-8B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Install from pip and serve model

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

Use Docker

docker model run hf.co/PIPer-iclr/PIPer-8B

SGLang

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

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

PIPer-8B / README.md

PIPer-iclr

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metadata

library_name: transformers
datasets:
  - PIPer-iclr/envbench-zeroshot-rl
  - PIPer-iclr/PIPer-SFT-2500-sharegpt
base_model:
  - PIPer-iclr/Qwen3-8B-am
pipeline_tag: text-generation
license: mit

🚀 PIPer: On-Device Environment Setup via Online Reinforcement Learning

Democratizing environment setup with on-device sized models that match the performance of much larger proprietary systems

🎯 Overview

Environment setup—the process of configuring systems to work with specific software projects—remains a persistent challenge in software engineering. PIPer addresses this by training specialized on-device models that can automatically generate correct Bash scripts for environment configuration.

Our approach combines:

📚 Supervised Fine-Tuning (SFT) with executable scripts from larger models
🎯 Reinforcement Learning with Verifiable Rewards (RLVR) using lightweight proxy LLM-reward

🏆 Key Results

Model	Size	EnvBench avg@5	Cost per 1M tokens
PIPer	8B	19.4	$0.60
GPT-4o	-	19.4	$15.00
Qwen3-32B	32B	16.2	$2.00
Qwen3-8B	8B	2.6	$0.60

🎉 PIPer achieves 9× improvement over its base model while matching GPT-4o performance at 25x lower cost

📦 Available Artifacts

🤖 Model Checkpoints

Model	Description	HuggingFace Link
🏅 PIPer (Full)	Complete SFT+RL trained model	PIPer-iclr/PIPer-8B
🎯 PIPer (RL-only)	RLVR checkpoint only	PIPer-iclr/PIPer-8B-RL-only
📚 PIPer (SFT-only)	Supervised fine-tuning only	PIPer-iclr/PIPer-8B-SFT-only

📊 Datasets

Dataset	Description	HuggingFace Link
EnvBench Zero-shot RL	Training prompts and evaluation data	PIPer-iclr/envbench-zeroshot-rl

📊 Evaluation Benchmarks

Benchmark	Description	Metric	Our Result
EnvBench-Python	329 Python repositories	pass@5	🏆 27/329
Repo2Run	420 Python repositories	pass@5	🏆 103/420
Terminal-Bench	80 terminal tasks	pass@10	4/80

🙏 Acknowledgments

Built on top of EnvBench evaluation framework
Uses VeRL for efficient RL training
Leverages Qwen3 model family as base architecture

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.