Instructions to use Wannita/baseline_codecompletion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Wannita/baseline_codecompletion with Transformers:

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

pipe = pipeline("text-generation", model="Wannita/baseline_codecompletion")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Wannita/baseline_codecompletion")
model = AutoModelForCausalLM.from_pretrained("Wannita/baseline_codecompletion", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Wannita/baseline_codecompletion with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Wannita/baseline_codecompletion

SGLang

How to use Wannita/baseline_codecompletion 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 "Wannita/baseline_codecompletion" \
    --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": "Wannita/baseline_codecompletion",
		"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 "Wannita/baseline_codecompletion" \
        --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": "Wannita/baseline_codecompletion",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Wannita/baseline_codecompletion with Docker Model Runner:
```
docker model run hf.co/Wannita/baseline_codecompletion
```

Wannita commited on Apr 18, 2023

Commit

11631d2

1 Parent(s): edd457a

Update README.md

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@@ -1,3 +1,12 @@
 # PyCoder
 This repository contains the baseline model for the paper [Syntax-Aware On-the-Fly Code Completion](https://arxiv.org/abs/2211.04673)
@@ -20,4 +29,4 @@ If you use our code or PyCoder, please cite our paper.
   author={Takerngsaksiri, Wannita and Tantithamthavorn, Chakkrit and Li, Yuan-Fang},
   journal={arXiv preprint arXiv:2211.04673},
   year={2022}
-}</code></pre>

+---
+datasets:
+- Wannita/PyCoder
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- python
+- code
+---
 # PyCoder
 This repository contains the baseline model for the paper [Syntax-Aware On-the-Fly Code Completion](https://arxiv.org/abs/2211.04673)
   author={Takerngsaksiri, Wannita and Tantithamthavorn, Chakkrit and Li, Yuan-Fang},
   journal={arXiv preprint arXiv:2211.04673},
   year={2022}
+}</code></pre>