Instructions to use madatnlp/torch-trinity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use madatnlp/torch-trinity with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="madatnlp/torch-trinity")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("madatnlp/torch-trinity") model = AutoModelForCausalLM.from_pretrained("madatnlp/torch-trinity", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use madatnlp/torch-trinity with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "madatnlp/torch-trinity" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "madatnlp/torch-trinity", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/madatnlp/torch-trinity
- SGLang
How to use madatnlp/torch-trinity 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 "madatnlp/torch-trinity" \ --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": "madatnlp/torch-trinity", "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 "madatnlp/torch-trinity" \ --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": "madatnlp/torch-trinity", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use madatnlp/torch-trinity with Docker Model Runner:
docker model run hf.co/madatnlp/torch-trinity
- Xet hash:
- f696b6d7c9b4de8f15b965a0fd902e3bd5c566668421a6bf50991e9318bcc7c9
- Size of remote file:
- 4.68 GB
- SHA256:
- 1bcdebe0e1a07883bc0a4d73c48a40995d627f1d03f3afbe81aa31897ccda9a2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.