Instructions to use rty813/laya-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rty813/laya-bf16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rty813/laya-bf16")# pip install -U transformers accelerate # Load model directly from transformers import LayaTypedDecisions model = LayaTypedDecisions.from_pretrained("rty813/laya-bf16", device_map="auto") - Laya
How to use rty813/laya-bf16 with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rty813/laya-bf16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rty813/laya-bf16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rty813/laya-bf16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rty813/laya-bf16
- SGLang
How to use rty813/laya-bf16 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 "rty813/laya-bf16" \ --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": "rty813/laya-bf16", "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 "rty813/laya-bf16" \ --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": "rty813/laya-bf16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use rty813/laya-bf16 with Docker Model Runner:
docker model run hf.co/rty813/laya-bf16
Download config.json from rty813/laya-bf16: direct link, hf CLI and curl.
- Browser
- Download file 195 Bytes
-
https://huggingface.co/rty813/laya-bf16/resolve/main/config.json
- Command line
-
hf download hf://rty813/laya-bf16/config.json
-
curl -L -o config.json https://huggingface.co/rty813/laya-bf16/resolve/main/config.json
195 Bytes
| { | |
| "model_type": "laya", | |
| "architectures": [ | |
| "LayaTypedDecisions" | |
| ], | |
| "_comment": "Root config for Hub download tracking. See README for how to load Laya.", | |
| "torch_dtype": "bfloat16" | |
| } |