Instructions to use claudios/CodeGPT-small-java-adaptedGPT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use claudios/CodeGPT-small-java-adaptedGPT2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="claudios/CodeGPT-small-java-adaptedGPT2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("claudios/CodeGPT-small-java-adaptedGPT2") model = AutoModelForCausalLM.from_pretrained("claudios/CodeGPT-small-java-adaptedGPT2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use claudios/CodeGPT-small-java-adaptedGPT2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "claudios/CodeGPT-small-java-adaptedGPT2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "claudios/CodeGPT-small-java-adaptedGPT2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/claudios/CodeGPT-small-java-adaptedGPT2
- SGLang
How to use claudios/CodeGPT-small-java-adaptedGPT2 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 "claudios/CodeGPT-small-java-adaptedGPT2" \ --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": "claudios/CodeGPT-small-java-adaptedGPT2", "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 "claudios/CodeGPT-small-java-adaptedGPT2" \ --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": "claudios/CodeGPT-small-java-adaptedGPT2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use claudios/CodeGPT-small-java-adaptedGPT2 with Docker Model Runner:
docker model run hf.co/claudios/CodeGPT-small-java-adaptedGPT2
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("claudios/CodeGPT-small-java-adaptedGPT2")
model = AutoModelForCausalLM.from_pretrained("claudios/CodeGPT-small-java-adaptedGPT2")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
This is an unofficial reupload of microsoft/CodeGPT-small-java-adaptedGPT2 in the SafeTensors format using transformers 4.40.1. The goal of this reupload is to prevent older models that are still relevant baselines from becoming stale as a result of changes in HuggingFace. Additionally, I may include minor corrections, such as model max length configuration.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="claudios/CodeGPT-small-java-adaptedGPT2")