Instructions to use JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py with Transformers:

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

pipe = pipeline("text-generation", model="JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py")
model = AutoModelForCausalLM.from_pretrained("JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py

SGLang

How to use JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py 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/OpenCoder-1.5B-Path-Distance-Py" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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/OpenCoder-1.5B-Path-Distance-Py" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py with Docker Model Runner:
```
docker model run hf.co/JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py
```

OpenCoder-1.5B-Path-Distance-Py / README.md

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	---
	license: other
	license_name: inf
	license_link: https://huggingface.co/infly/OpenCoder-1.5B-Base/blob/main/LICENSE
	language:
	- en
	- zh
	base_model: infly/OpenCoder-1.5B-Base
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- code
	---

	## Description

	This model is derived from [OpenCoder-1.5B-Base](https://huggingface.co/infly/OpenCoder-1.5B-Base) by applying additional context extension fine-tuning. The repository context is composed using the _Path Distance `.py`_ composer, more details on which, along with others, can be found in the [On Pretraining for Project-Level Code Completion](https://openreview.net/forum?id=t9RN9WX4Ic) paper ([arxiv](https://arxiv.org/abs/2510.13697)). Specifically, Section A.1 of the Appendix describes the context composition method, and Table 3 provides a comparison with other composers from the same [collection](https://huggingface.co/collections/JetBrains-Research/repository-level-pre-trained-opencoder-68e938c003be1cfba9c3595e).

	We publish this checkpoint to support the reproducibility and accessibility of our research results.

	## Quickstart

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "JetBrains-Research/OpenCoder-1.5B-Path-Distance-Py"
	tokenizer_name = "infly/OpenCoder-1.5B-Base"

	model = AutoModelForCausalLM.from_pretrained(model_name,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	trust_remote_code=True)
	tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, trust_remote_code=True)

	inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
	outputs = model.generate(**inputs.to(model.device), max_new_tokens=256)

	result = tokenizer.decode(outputs[0], skip_special_tokens=True)
	print(result)
	```