Instructions to use transformersbook/codeparrot-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use transformersbook/codeparrot-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="transformersbook/codeparrot-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("transformersbook/codeparrot-small") model = AutoModelForCausalLM.from_pretrained("transformersbook/codeparrot-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use transformersbook/codeparrot-small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "transformersbook/codeparrot-small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "transformersbook/codeparrot-small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/transformersbook/codeparrot-small
- SGLang
How to use transformersbook/codeparrot-small 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 "transformersbook/codeparrot-small" \ --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": "transformersbook/codeparrot-small", "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 "transformersbook/codeparrot-small" \ --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": "transformersbook/codeparrot-small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use transformersbook/codeparrot-small with Docker Model Runner:
docker model run hf.co/transformersbook/codeparrot-small
Commit History
-
Librarian Bot: Update dataset YAML metadata for model
This is a pull request to add a dataset, [`transformersbook/codeparrot`](https://huggingface.co/datasets/transformersbook/codeparrot), to the metadata for your model (defined in the `YAML` block of your model's `README.md`). The pull request was made by [librarian-bot](https://huggingface.co/librarian-bot) and used a combination of rules and/or machine learning to suggest this additional metadata. If this suggestion is incorrect, feel free to close this pull request. Librarian Bot was made by [@davanstrien](https://huggingface.co/davanstrien); feel free to get in touch with feedback.0e5e5a0
-
Create README.md
4e8cbf6
-
final model
08f3a18 -
step 150000
feefd63 -
step 135000
2ab1d33 -
step 120000
ad36a2c -
step 105000
5253b38 -
step 90000
8d530b8 -
step 75000
80fd2f3 -
step 60000
0fffc71
-
step 45000
5c39c41 -
step 30000
a0966e8 -
step 15000
bfee2e5
-
fix typo
cd6f029 -
update training script and requirements
leandro von werrae3d9b66
-
add requirements.txt
12f24f2 -
add training script
c8dd7f5 -
add tokenizer
c9c953d
-
add model
leandro von werra7edb85b -
initial commit
0ff7d3d