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 / dataset_summary.json

thealper2

Upload folder using huggingface_hub

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	{
	"dataset_name": "google/code_x_glue_cc_code_refinement",
	"dataset_config": "small",
	"splits": {
	"train": 46680,
	"validation": 5835,
	"test": 5835
	},
	"source_column": "buggy",
	"target_column": "fixed",
	"detected_language": "java (heuristic, score=1193)",
	"columns": [
	"id",
	"buggy",
	"fixed"
	],
	"malformed": {
	"train": 0,
	"validation": 0,
	"test": 0
	},
	"identical_pairs": {
	"train": 0,
	"validation": 0,
	"test": 0
	},
	"length_stats": {
	"train/buggy": {
	"mean": 53.388110539845755,
	"p50": 53.0,
	"p95": 82.0,
	"p99": 92.0,
	"max": 132
	},
	"train/fixed": {
	"mean": 48.23601113967438,
	"p50": 47.0,
	"p95": 79.0,
	"p99": 89.0,
	"max": 124
	},
	"validation/buggy": {
	"mean": 53.56966580976864,
	"p50": 53.0,
	"p95": 82.0,
	"p99": 91.65999999999985,
	"max": 119
	},
	"validation/fixed": {
	"mean": 48.40222793487575,
	"p50": 48.0,
	"p95": 79.0,
	"p99": 87.0,
	"max": 111
	},
	"test/buggy": {
	"mean": 53.23547557840617,
	"p50": 53.0,
	"p95": 83.0,
	"p99": 92.0,
	"max": 128
	},
	"test/fixed": {
	"mean": 48.16915167095116,
	"p50": 48.0,
	"p95": 78.0,
	"p99": 88.0,
	"max": 130
	}
	},
	"truncated": {
	"train": {
	"source_truncated": 0,
	"target_truncated": 0,
	"total": 46680
	},
	"validation": {
	"source_truncated": 0,
	"target_truncated": 0,
	"total": 5835
	},
	"test": {
	"source_truncated": 0,
	"target_truncated": 0,
	"total": 5835
	}
	}
	}