Instructions to use Bashitha25/MotivateDHR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bashitha25/MotivateDHR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Bashitha25/MotivateDHR")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Bashitha25/MotivateDHR", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Bashitha25/MotivateDHR with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Bashitha25/MotivateDHR" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Bashitha25/MotivateDHR", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Bashitha25/MotivateDHR
- SGLang
How to use Bashitha25/MotivateDHR 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 "Bashitha25/MotivateDHR" \ --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": "Bashitha25/MotivateDHR", "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 "Bashitha25/MotivateDHR" \ --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": "Bashitha25/MotivateDHR", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Bashitha25/MotivateDHR with Docker Model Runner:
docker model run hf.co/Bashitha25/MotivateDHR
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Bashitha25/MotivateDHR", dtype="auto")Quick Links
MotivateDHR
MotivateDHR is a motivational bot designed to deliver uplifting and inspirational quotes to users. It aims to provide a daily boost of positivity and encouragement through well-crafted messages.
Model Overview
- Purpose: To inspire and uplift individuals with motivational quotes and messages.
- Key Features:
- Provides daily motivational quotes.
- Customizable for different motivational themes and user preferences.
- Easy integration with various applications and platforms.
Usage
You can use MotivateDHR through the Hugging Face API or load it locally using the transformers library.
API Usage
Here’s an example of how to use the model via the Hugging Face API:
from transformers import pipeline
# Initialize the model
model_name = "Bashitha25/MotivateDHR"
generator = pipeline('text-generation', model=model_name)
# Generate a motivational quote
quote = generator("Start your day with a positive thought:", max_length=50)
print(quote)
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Bashitha25/MotivateDHR")