Text Generation
Transformers
Safetensors
English
code
t5
text2text-generation
security
vulnerability-fix
code-repair
code-generation
codet5
owasp
cwe
Eval Results (legacy)
text-generation-inference
Instructions to use ayshajavd/codet5p-vuln-fixer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ayshajavd/codet5p-vuln-fixer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ayshajavd/codet5p-vuln-fixer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ayshajavd/codet5p-vuln-fixer") model = AutoModelForSeq2SeqLM.from_pretrained("ayshajavd/codet5p-vuln-fixer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ayshajavd/codet5p-vuln-fixer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ayshajavd/codet5p-vuln-fixer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ayshajavd/codet5p-vuln-fixer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ayshajavd/codet5p-vuln-fixer
- SGLang
How to use ayshajavd/codet5p-vuln-fixer 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 "ayshajavd/codet5p-vuln-fixer" \ --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": "ayshajavd/codet5p-vuln-fixer", "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 "ayshajavd/codet5p-vuln-fixer" \ --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": "ayshajavd/codet5p-vuln-fixer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ayshajavd/codet5p-vuln-fixer with Docker Model Runner:
docker model run hf.co/ayshajavd/codet5p-vuln-fixer
Download training_args.bin from ayshajavd/codet5p-vuln-fixer: direct link, hf CLI and curl.
- Browser
- Download file 5.46 kB
-
https://huggingface.co/ayshajavd/codet5p-vuln-fixer/resolve/main/training_args.bin
- Command line
-
hf download hf://ayshajavd/codet5p-vuln-fixer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ayshajavd/codet5p-vuln-fixer/resolve/main/training_args.bin
5.46 kB
- Xet hash:
- 20091042506439576672640ad5be3aadb1bff0189b35a54b8ccfd1d3542b939d
- Size of remote file:
- 5.46 kB
- SHA256:
- 2fc43deeeeebf7b748bbabca42870ac0af473d60273a43276d922d27a47e4e32
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