Instructions to use braxtongough/HandyAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use braxtongough/HandyAI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="braxtongough/HandyAI")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("braxtongough/HandyAI", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use braxtongough/HandyAI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "braxtongough/HandyAI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "braxtongough/HandyAI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/braxtongough/HandyAI
- SGLang
How to use braxtongough/HandyAI 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 "braxtongough/HandyAI" \ --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": "braxtongough/HandyAI", "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 "braxtongough/HandyAI" \ --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": "braxtongough/HandyAI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use braxtongough/HandyAI with Docker Model Runner:
docker model run hf.co/braxtongough/HandyAI
HandyAI is a versatile, all-purpose AI model designed to assist users across a wide range of tasks. Whether you need help with text generation, answering questions, summarizing information, or brainstorming ideas, HandyAI is built to provide quick, accurate, and intelligent responses.
🔹 Features • General-Purpose Utility: HandyAI is not limited to a single task—it can be used for conversation, creative writing, coding help, and more. • User-Friendly: Designed to be easy to use, whether integrated into a chatbot, website, or standalone application. • Fast & Efficient: Provides quick responses while maintaining accuracy and coherence. • Adaptive Learning: Continuously improved based on user interactions and feedback.
🔹 Use Cases • Chatbot Integration: Use HandyAI to power intelligent chatbots for websites, Discord, or other platforms. • Content Generation: Generate articles, summaries, stories, or even scripts with minimal effort. • Code Assistance: Get explanations, generate code snippets, or debug issues in programming. • Productivity Boost: Assist with note-taking, task automation, and information retrieval.
🔹 Technical Details • Model Type: Transformer-based AI model • Training Data: Trained on a diverse dataset to ensure broad knowledge and contextual understanding • Deployment: Can be used via Hugging Face API, locally, or integrated into web applications
🔹 How to Use
You can interact with HandyAI via API calls, embed it in your website, or deploy it in a chatbot framework. Check out the implementation guide and example code to get started.
HandyAI is designed to be your go-to AI assistant for everyday tasks, making interactions smarter, faster, and more convenient. 🚀
DISCLAIMER: Some of these features are in development and are goals, these may be implemented in the future or may not.