Instructions to use thealper2/plbart-docstring-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thealper2/plbart-docstring-generation with Transformers:

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

pipe = pipeline("text-generation", model="thealper2/plbart-docstring-generation")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("thealper2/plbart-docstring-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("thealper2/plbart-docstring-generation", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thealper2/plbart-docstring-generation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thealper2/plbart-docstring-generation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thealper2/plbart-docstring-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/thealper2/plbart-docstring-generation

SGLang

How to use thealper2/plbart-docstring-generation 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 "thealper2/plbart-docstring-generation" \
    --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": "thealper2/plbart-docstring-generation",
		"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 "thealper2/plbart-docstring-generation" \
        --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": "thealper2/plbart-docstring-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use thealper2/plbart-docstring-generation with Docker Model Runner:
```
docker model run hf.co/thealper2/plbart-docstring-generation
```

plbart-docstring-generation / README.md

thealper2

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	---
	language:
	- en
	- code
	library_name: transformers
	pipeline_tag: text-generation
	base_model: uclanlp/plbart-base
	datasets:
	- semeru/code-text-python
	tags:
	- plbart
	- code-summarization
	- docstring-generation
	- python
	metrics:
	- bleu
	- rouge
	model-index:
	- name: thealper2/plbart-docstring-generation
	results:
	- task:
	type: text2text-generation
	name: Python docstring generation
	dataset:
	name: semeru/code-text-python
	type: semeru/code-text-python
	split: test
	metrics:
	- type: bleu
	value: 5.9444
	name: BLEU
	- type: rouge1
	value: 34.7862
	name: ROUGE-1
	- type: rouge2
	value: 12.6668
	name: ROUGE-2
	- type: rougeL
	value: 32.0423
	name: ROUGE-L
	---

	# plbart-docstring-generation

	[`uclanlp/plbart-base`](https://huggingface.co/uclanlp/plbart-base) fully fine-tuned to generate English docstrings for Python functions, trained on [`semeru/code-text-python`](https://huggingface.co/datasets/semeru/code-text-python).

	## Usage

	```python
	from transformers import AutoTokenizer, PLBartForConditionalGeneration

	tokenizer = AutoTokenizer.from_pretrained("thealper2/plbart-docstring-generation", src_lang="python", tgt_lang="en_XX")
	model = PLBartForConditionalGeneration.from_pretrained("thealper2/plbart-docstring-generation")

	code = "def add(a, b):\n return a + b"
	inputs = tokenizer(" ".join(code.split()), max_length=512, truncation=True, return_tensors="pt")
	out = model.generate(**inputs, num_beams=4, max_length=64,
	decoder_start_token_id=model.config.decoder_start_token_id)
	print(tokenizer.decode(out[0], skip_special_tokens=True))
	```

	## Evaluation

	Test split (14918 examples), beam search with 4 beams, max length 64.

	\| Split \| BLEU \| ROUGE-1 \| ROUGE-2 \| ROUGE-L \| Loss \|
	\|---\|---\|---\|---\|---\|---\|
	\| test \| 5.94 \| 34.79 \| 12.67 \| 32.04 \| 2.6885 \|
	\| validation \| 5.46 \| 33.95 \| 12.33 \| 31.28 \| 3.8836 \|

	Mean generated length: 6.35 tokens (references: 11.20).

	## Training

	\| Hyperparameter \| Value \|
	\|---\|---\|
	\| max_train_samples \| 50000 \|
	\| num_epochs \| 2.0 \|
	\| learning_rate \| 3e-05 \|
	\| train_batch_size \| 32 \|
	\| gradient_accumulation_steps \| 1 \|
	\| weight_decay \| 0.01 \|
	\| warmup_ratio \| 0.05 \|
	\| lr_scheduler_type \| linear \|
	\| label_smoothing_factor \| 0.1 \|
	\| max_source_length \| 512 \|
	\| max_target_length \| 128 \|
	\| bf16 \| True \|
	\| seed \| 42 \|

	Trained examples: 50000. Training time: 0.29 h on NVIDIA GeForce RTX 5060 Ti (15.9 GiB, sm_120).

	## Limitations

	Generated docstrings are short, single-sentence summaries; they tend to be shorter than human-written references and may describe parameters or behaviour incorrectly. Review them before use.