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

Libraries

How to use JetBrains-Research/OpenCoder-1.5B-Duplication with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JetBrains-Research/OpenCoder-1.5B-Duplication")
model = AutoModelForCausalLM.from_pretrained("JetBrains-Research/OpenCoder-1.5B-Duplication", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JetBrains-Research/OpenCoder-1.5B-Duplication 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-Duplication"
# 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-Duplication",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JetBrains-Research/OpenCoder-1.5B-Duplication

SGLang

How to use JetBrains-Research/OpenCoder-1.5B-Duplication 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-Duplication" \
    --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-Duplication",
		"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-Duplication" \
        --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-Duplication",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

OpenCoder-1.5B-Duplication / model-00002-of-00002.safetensors

sapromak

Upload LlamaForCausalLM

c67acb9 verified 10 months ago

Download

History Blame Contribute Delete

2.64 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: d1c7a8a07248d08124c9cc8dc644a40a418ae87de388b210e65b7a75797102a3
Size of remote file:: 2.64 GB
SHA256:: ee7aeab8f9d9cbccdf9d4ce7d349aba569a9918351a1f39450659f06a7eca5a4

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.