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
```

thealper2 commited on Sep 22

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+---
+language:
+- en
+- code
+library_name: transformers
+pipeline_tag: text2text-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.