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")

# pip install -U transformers accelerate
# 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

uclanlp/plbart-base fully fine-tuned to generate English docstrings for Python functions, trained on semeru/code-text-python.

Usage

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.

Downloads last month: 138

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for thealper2/plbart-docstring-generation

Base model

uclanlp/plbart-base

Finetuned

(30)

this model

Dataset used to train thealper2/plbart-docstring-generation

Evaluation results

BLEU on semeru/code-text-python
test set self-reported

5.944
ROUGE-1 on semeru/code-text-python
test set self-reported

34.786
ROUGE-2 on semeru/code-text-python
test set self-reported

12.667
ROUGE-L on semeru/code-text-python
test set self-reported

32.042