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
```

NinedayWang

urialon commited on Oct 20, 2022

Commit

6523eda

1 Parent(s): 5c27061

Create README.md (#1)

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- Create README.md (fe704dd224fa056da1637c330081f6a4382ba90c)

Co-authored-by: Uri Alon <urialon@users.noreply.huggingface.co>

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+This is a PolyCoder model with **2.7B** parameters,
+presented in the paper ["A Systematic Evaluation of Large Language Models of Code"](https://arxiv.org/pdf/2202.13169.pdf) (MAPS'2022 and ICLR'2022 Workshop Deep Learning 4 Code).
+The model was trained on **249 GB** of code across **12** programming languages.
+For more information, see: [https://github.com/VHellendoorn/Code-LMs](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}
+}
+```