Instructions to use thealper2/t5-base-code-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thealper2/t5-base-code-summarization with Transformers:

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

pipe = pipeline("text-generation", model="thealper2/t5-base-code-summarization")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("thealper2/t5-base-code-summarization")
model = AutoModelForSeq2SeqLM.from_pretrained("thealper2/t5-base-code-summarization", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thealper2/t5-base-code-summarization with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thealper2/t5-base-code-summarization"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thealper2/t5-base-code-summarization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/thealper2/t5-base-code-summarization

SGLang

How to use thealper2/t5-base-code-summarization 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/t5-base-code-summarization" \
    --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/t5-base-code-summarization",
		"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/t5-base-code-summarization" \
        --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/t5-base-code-summarization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use thealper2/t5-base-code-summarization with Docker Model Runner:
```
docker model run hf.co/thealper2/t5-base-code-summarization
```

t5-base-code-summarization / generation_config.json

thealper2

Upload T5ForConditionalGeneration

3b4089c verified 5 days ago

Raw

History Blame Contribute Delete

385 Bytes

	{
	"_from_model_config": true,
	"decoder_start_token_id": 0,
	"do_sample": false,
	"early_stopping": true,
	"eos_token_id": 1,
	"length_penalty": 1.0,
	"max_length": 64,
	"min_length": 4,
	"no_repeat_ngram_size": 3,
	"num_beams": 4,
	"output_attentions": false,
	"output_hidden_states": false,
	"pad_token_id": 0,
	"transformers_version": "5.17.0",
	"use_cache": true
	}