Text Generation
Transformers
Safetensors
English
t5
text2text-generation
commit-message-generation
summarization
code
text-generation-inference
Instructions to use thealper2/t5-small-commitbench with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thealper2/t5-small-commitbench with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="thealper2/t5-small-commitbench")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("thealper2/t5-small-commitbench") model = AutoModelForSeq2SeqLM.from_pretrained("thealper2/t5-small-commitbench", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use thealper2/t5-small-commitbench with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "thealper2/t5-small-commitbench" # 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-small-commitbench", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/thealper2/t5-small-commitbench
- SGLang
How to use thealper2/t5-small-commitbench 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-small-commitbench" \ --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-small-commitbench", "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-small-commitbench" \ --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-small-commitbench", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use thealper2/t5-small-commitbench with Docker Model Runner:
docker model run hf.co/thealper2/t5-small-commitbench
Download run_config.json from thealper2/t5-small-commitbench: direct link, hf CLI and curl.
- Browser
- Download file 1.59 kB
-
https://huggingface.co/thealper2/t5-small-commitbench/resolve/main/run_config.json
- Command line
-
hf download hf://thealper2/t5-small-commitbench/run_config.json
-
curl -L -o run_config.json https://huggingface.co/thealper2/t5-small-commitbench/resolve/main/run_config.json
1.59 kB
| { | |
| "data": { | |
| "dataset_name": "Maxscha/commitbench", | |
| "cache_dir": null, | |
| "prefix": "generate commit message: ", | |
| "target_mode": "full", | |
| "max_source_length": 512, | |
| "max_target_length": 64, | |
| "min_diff_chars": 1, | |
| "min_message_chars": 1, | |
| "num_proc": 8, | |
| "max_train_samples": 500000, | |
| "max_eval_samples": 2000, | |
| "max_predict_samples": null | |
| }, | |
| "train": { | |
| "model_name": "google-t5/t5-small", | |
| "output_dir": "outputs/t5-small-commitbench", | |
| "seed": 42, | |
| "deterministic": false, | |
| "learning_rate": 3e-05, | |
| "num_train_epochs": 2.0, | |
| "per_device_train_batch_size": 32, | |
| "per_device_eval_batch_size": 64, | |
| "gradient_accumulation_steps": 1, | |
| "weight_decay": 0.01, | |
| "warmup_ratio": 0.05, | |
| "label_smoothing_factor": 0.0, | |
| "max_grad_norm": 1.0, | |
| "lr_scheduler_type": "linear", | |
| "eval_strategy": "steps", | |
| "save_strategy": "steps", | |
| "eval_steps": 3000, | |
| "save_steps": 3000, | |
| "logging_steps": 250, | |
| "save_total_limit": 2, | |
| "metric_for_best_model": "eval_loss", | |
| "greater_is_better": false, | |
| "load_best_model_at_end": true, | |
| "gradient_checkpointing": false, | |
| "torch_compile": true, | |
| "torch_compile_mode": "", | |
| "group_by_length": true, | |
| "dataloader_num_workers": 4, | |
| "precision": "auto", | |
| "resume_from_checkpoint": null, | |
| "max_steps": -1 | |
| }, | |
| "generation": { | |
| "num_beams": 4, | |
| "max_new_tokens": 64, | |
| "min_new_tokens": 0, | |
| "length_penalty": 1.0, | |
| "no_repeat_ngram_size": 3, | |
| "early_stopping": true, | |
| "do_sample": false, | |
| "eval_num_beams": 1 | |
| } | |
| } |