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
```

sapromak commited on Oct 17, 2025

Commit

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@@ -14,7 +14,7 @@ tags:
 ## 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. 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.

 ## 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.