Instructions to use codeparrot/codeparrot-small-text-to-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codeparrot/codeparrot-small-text-to-code with Transformers:

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

pipe = pipeline("text-generation", model="codeparrot/codeparrot-small-text-to-code")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-text-to-code")
model = AutoModelForCausalLM.from_pretrained("codeparrot/codeparrot-small-text-to-code")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use codeparrot/codeparrot-small-text-to-code with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/codeparrot/codeparrot-small-text-to-code

SGLang

How to use codeparrot/codeparrot-small-text-to-code 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 "codeparrot/codeparrot-small-text-to-code" \
    --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": "codeparrot/codeparrot-small-text-to-code",
		"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 "codeparrot/codeparrot-small-text-to-code" \
        --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": "codeparrot/codeparrot-small-text-to-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use codeparrot/codeparrot-small-text-to-code with Docker Model Runner:
```
docker model run hf.co/codeparrot/codeparrot-small-text-to-code
```

codeparrot-small-text-to-code / README.md

loubnabnl HF Staff

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metadata

language:
  - code
license: apache-2.0
tags:
  - code
  - gpt2
  - generation
datasets:
  - codeparrot/codeparrot-clean
  - codeparrot/github-jupyter-text-to-code

CodeParrot 🦜 small for text-t-code generation

This model is CodeParrot-small (from branch megatron) Fine-tuned on github-jupyter-text-to-code, a dataset where the samples are a succession of docstrings and their Python code, originally extracted from Jupyter notebooks parsed in this dataset.