Instructions to use loganrobbins/parallel-decoder-transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use loganrobbins/parallel-decoder-transformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="loganrobbins/parallel-decoder-transformer")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("loganrobbins/parallel-decoder-transformer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use loganrobbins/parallel-decoder-transformer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "loganrobbins/parallel-decoder-transformer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "loganrobbins/parallel-decoder-transformer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/loganrobbins/parallel-decoder-transformer
- SGLang
How to use loganrobbins/parallel-decoder-transformer 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 "loganrobbins/parallel-decoder-transformer" \ --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": "loganrobbins/parallel-decoder-transformer", "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 "loganrobbins/parallel-decoder-transformer" \ --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": "loganrobbins/parallel-decoder-transformer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use loganrobbins/parallel-decoder-transformer with Docker Model Runner:
docker model run hf.co/loganrobbins/parallel-decoder-transformer
| 3f4233a967733a2dc9c9a48bae4d62d68a5fd009725714bff64fe7ffcb079777 README.md | |
| 1480ecbcfb0f893fb503d596457cbca4ba0a953b428f1b9e3c77aea36b509655 agreement_thresholds.json | |
| ffffed033e7c8abdca80e258cb261e70b784bc1efc3b3fe3cdd49bc44c9ccb75 pdt_adapters.safetensors | |
| ac5a69de8b61c55db51bd693544f4dec7598b86c4a50b7f2a0e4ff4dc9ce1366 train_manifest.json | |
| c5e3ad48156d856426d85ebf30697f2e6e5da33d15f11b378dc28ee042446d40 train_run_stages.json | |
| 5220c01bd48ceaf072a7ba0262964ecb6c61d8f589ffa28e4b0442c3eccd02f1 training_report.json | |