Instructions to use NinedayWang/PolyCoder-2.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NinedayWang/PolyCoder-2.7B with Transformers:

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

pipe = pipeline("text-generation", model="NinedayWang/PolyCoder-2.7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NinedayWang/PolyCoder-2.7B")
model = AutoModelForCausalLM.from_pretrained("NinedayWang/PolyCoder-2.7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NinedayWang/PolyCoder-2.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NinedayWang/PolyCoder-2.7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NinedayWang/PolyCoder-2.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NinedayWang/PolyCoder-2.7B

SGLang

How to use NinedayWang/PolyCoder-2.7B 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 "NinedayWang/PolyCoder-2.7B" \
    --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": "NinedayWang/PolyCoder-2.7B",
		"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 "NinedayWang/PolyCoder-2.7B" \
        --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": "NinedayWang/PolyCoder-2.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NinedayWang/PolyCoder-2.7B with Docker Model Runner:
```
docker model run hf.co/NinedayWang/PolyCoder-2.7B
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

This is a PolyCoder model with 2.7B parameters, presented in the paper "A Systematic Evaluation of Large Language Models of Code" (MAPS'2022 and ICLR'2022 Workshop Deep Learning 4 Code).

The model was trained on 249 GB of code across 12 programming languages.

Note - this model requires transformers version of at least 4.23.0:

pip install transformers==4.23.0

For more information, see: https://github.com/VHellendoorn/Code-LMs

If you use this model, please cite:

@inproceedings{
  xu2022polycoder,
  title={A Systematic Evaluation of Large Language Models of Code},
  author={Frank F. Xu and Uri Alon and Graham Neubig and Vincent Josua Hellendoorn},
  booktitle={Deep Learning for Code Workshop},
  year={2022},
  url={https://openreview.net/forum?id=SLcEnoObJZq}
}

Downloads last month: 1,593

Model tree for NinedayWang/PolyCoder-2.7B

Adapters

1 model

Spaces using NinedayWang/PolyCoder-2.7B 16

Paper for NinedayWang/PolyCoder-2.7B

A Systematic Evaluation of Large Language Models of Code

Paper • 2202.13169 • Published Feb 26, 2022 • 1