Instructions to use thealper2/codet5-base-code-repair with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thealper2/codet5-base-code-repair with Transformers:

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

pipe = pipeline("text-generation", model="thealper2/codet5-base-code-repair")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("thealper2/codet5-base-code-repair")
model = AutoModelForSeq2SeqLM.from_pretrained("thealper2/codet5-base-code-repair", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thealper2/codet5-base-code-repair with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thealper2/codet5-base-code-repair"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thealper2/codet5-base-code-repair",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/thealper2/codet5-base-code-repair

SGLang

How to use thealper2/codet5-base-code-repair 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/codet5-base-code-repair" \
    --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/codet5-base-code-repair",
		"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/codet5-base-code-repair" \
        --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/codet5-base-code-repair",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use thealper2/codet5-base-code-repair with Docker Model Runner:
```
docker model run hf.co/thealper2/codet5-base-code-repair
```

codet5-base-code-repair

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{
  "model_name": "Salesforce/codet5-base",
  "dataset_name": "google/code_x_glue_cc_code_refinement",
  "dataset_config": "small",
  "source_column": null,
  "target_column": null,
  "language": "java",
  "train_split": "train",
  "eval_split": "validation",
  "test_split": "test",
  "max_source_length": 256,
  "max_target_length": 256,
  "pad_to_multiple_of": 8,
  "learning_rate": 5e-05,
  "per_device_train_batch_size": 16,
  "per_device_eval_batch_size": 64,
  "gradient_accumulation_steps": 2,
  "num_train_epochs": 10.0,
  "weight_decay": 0.01,
  "warmup_ratio": 0.05,
  "lr_scheduler_type": "linear",
  "max_grad_norm": 1.0,
  "label_smoothing_factor": 0.0,
  "gradient_checkpointing": false,
  "eval_strategy": "epoch",
  "save_strategy": "epoch",
  "logging_steps": 100,
  "save_total_limit": 2,
  "load_best_model_at_end": true,
  "metric_for_best_model": "exact_match",
  "greater_is_better": true,
  "early_stopping_patience": 3,
  "eval_subset_size": 1000,
  "max_eval_examples": 0,
  "num_beams": 4,
  "max_new_tokens": 256,
  "generation_early_stopping": true,
  "length_penalty": 1.0,
  "no_repeat_ngram_size": 0,
  "seed": 42,
  "output_dir": "outputs/codet5-base-code-repair",
  "precision": "auto",
  "dataloader_num_workers": 4,
  "report_to": "none",
  "resume_from_checkpoint": null,
  "compute_codebleu": true,
  "smoke_test": false,
  "smoke_train_size": 256,
  "smoke_eval_size": 32,
  "smoke_epochs": 1.0
}