Text Generation
Transformers
Safetensors
code
t5
text2text-generation
codet5
code-repair
program-repair
bug-fixing
java
seq2seq
Eval Results (legacy)
text-generation-inference
Instructions to use thealper2/codet5-base-code-repair with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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")# 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
File size: 3,530 Bytes
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"reference": "public static TYPE_1 METHOD_1 ( final TYPE_2 VAR_1 , final java.lang.Object msg ) { TYPE_3 VAR_2 = TYPE_4 . METHOD_2 ( VAR_1 ) ; return TYPE_5 . METHOD_1 ( VAR_2 , msg , null ) ; }",
"prediction": "public static TYPE_1 METHOD_1 ( final TYPE_2 VAR_1 , final java.lang.Object msg ) { TYPE_3 VAR_2 = TYPE_4 . METHOD_2 ( VAR_1 ) ; return TYPE_5 . METHOD_1 ( VAR_2 , msg , null ) ; }"
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"buggy": "public void METHOD_1 ( ) { TYPE_1 . METHOD_2 ( VAR_1 , STRING_1 ) ; METHOD_3 ( false ) ; METHOD_4 ( ) ; super . METHOD_1 ( ) ; } \n",
"reference": "public void METHOD_1 ( ) { TYPE_1 . METHOD_2 ( VAR_1 , STRING_1 ) ; METHOD_4 ( ) ; super . METHOD_1 ( ) ; }",
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"bucket": "partial",
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"reference": "public java.util.List < TYPE_1 > METHOD_1 ( ) { return VAR_1 ; }",
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"buggy": "public static void main ( java.lang.String [ ] args ) throws java.lang.Exception { TYPE_1 VAR_1 = new TYPE_1 ( ) ; VAR_1 . METHOD_1 ( ) ; VAR_1 . add ( VAR_2 ) ; VAR_1 . METHOD_2 ( true ) ; VAR_1 . init ( STRING_1 ) ; } \n",
"reference": "public static void main ( java.lang.String [ ] args ) throws java.lang.Exception { TYPE_1 VAR_1 = new TYPE_1 ( ) ; VAR_1 . METHOD_1 ( ) ; VAR_1 . METHOD_2 ( true ) ; VAR_1 . init ( STRING_1 ) ; }",
"prediction": "public static void main ( java.lang.String [ ] args ) throws java.lang.Exception { TYPE_1 VAR_1 = new TYPE_1 ( ) ; VAR_1 . METHOD_1 ( ) ; VAR_1 . add ( VAR_2 ) ; VAR_1 . init ( STRING_1 ) ; }"
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"reference": "public TYPE_1 < TYPE_2 > METHOD_1 ( TYPE_3 VAR_1 , java.lang.String VAR_2 , java.util.HashMap < java.lang.String , java.lang.String > parameters ) { return METHOD_1 ( VAR_1 . toString ( ) , VAR_2 , parameters ) ; }",
"prediction": "public TYPE_1 < TYPE_2 > METHOD_1 ( TYPE_3 VAR_1 , java.lang.String VAR_2 ) { return METHOD_1 ( VAR_1 , VAR_2 , null ) ; }"
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"reference": "public void METHOD_1 ( int VAR_1 , java.lang.String VAR_2 , long VAR_3 ) { if ( VAR_1 == 0 ) { VAR_4 . METHOD_2 ( java.lang.String . METHOD_3 ( VAR_3 ) ) ; } else { VAR_4 . error ( VAR_1 ) ; } }",
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