Text Generation
Transformers
PyTorch
Safetensors
gpt_bigcode
code
Eval Results (legacy)
text-generation-inference
Instructions to use nuprl/MultiPL-T-StarCoderBase_15b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nuprl/MultiPL-T-StarCoderBase_15b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nuprl/MultiPL-T-StarCoderBase_15b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nuprl/MultiPL-T-StarCoderBase_15b") model = AutoModelForCausalLM.from_pretrained("nuprl/MultiPL-T-StarCoderBase_15b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use nuprl/MultiPL-T-StarCoderBase_15b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nuprl/MultiPL-T-StarCoderBase_15b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nuprl/MultiPL-T-StarCoderBase_15b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nuprl/MultiPL-T-StarCoderBase_15b
- SGLang
How to use nuprl/MultiPL-T-StarCoderBase_15b 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 "nuprl/MultiPL-T-StarCoderBase_15b" \ --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": "nuprl/MultiPL-T-StarCoderBase_15b", "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 "nuprl/MultiPL-T-StarCoderBase_15b" \ --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": "nuprl/MultiPL-T-StarCoderBase_15b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nuprl/MultiPL-T-StarCoderBase_15b with Docker Model Runner:
docker model run hf.co/nuprl/MultiPL-T-StarCoderBase_15b
- Xet hash:
- 5aee1fc668925b52c286f6857e01169bc79aae4dede0ce6130a2ce86981c597d
- Size of remote file:
- 9.86 GB
- SHA256:
- a62349d2e462abd2f53676ce2e5ab5e7a185a839e31565b78ad61671ef014778
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.