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
Download training_args.bin from thealper2/codet5-base-code-repair: direct link, hf CLI and curl.
- Browser
- Download file 7.31 kB
-
https://huggingface.co/thealper2/codet5-base-code-repair/resolve/main/training_args.bin
- Command line
-
hf download hf://thealper2/codet5-base-code-repair/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/thealper2/codet5-base-code-repair/resolve/main/training_args.bin
7.31 kB
- Xet hash:
- a00ccd3c20231f21a9bec151724f4887cdb843b95d0f8298ab765b66a79eef38
- Size of remote file:
- 7.31 kB
- SHA256:
- b21c5d4e30108428603fa9c4b5c15ba7bde35035f2f7c01b9d2ed30b839d952b
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