Instructions to use shahules786/Safetybot-mt5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shahules786/Safetybot-mt5-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shahules786/Safetybot-mt5-base") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("shahules786/Safetybot-mt5-base") model = AutoModelForMultimodalLM.from_pretrained("shahules786/Safetybot-mt5-base") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use shahules786/Safetybot-mt5-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shahules786/Safetybot-mt5-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shahules786/Safetybot-mt5-base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/shahules786/Safetybot-mt5-base
- SGLang
How to use shahules786/Safetybot-mt5-base 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 "shahules786/Safetybot-mt5-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shahules786/Safetybot-mt5-base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "shahules786/Safetybot-mt5-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shahules786/Safetybot-mt5-base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use shahules786/Safetybot-mt5-base with Docker Model Runner:
docker model run hf.co/shahules786/Safetybot-mt5-base
| { | |
| "additional_special_tokens": null, | |
| "eos_token": "</s>", | |
| "extra_ids": 0, | |
| "model_max_length": 512, | |
| "name_or_path": "google/mt5-base", | |
| "pad_token": "<pad>", | |
| "padding_side": "right", | |
| "sp_model_kwargs": {}, | |
| "special_tokens_map_file": "/home/patrick/.cache/torch/transformers/685ac0ca8568ec593a48b61b0a3c272beee9bc194a3c7241d15dcadb5f875e53.f76030f3ec1b96a8199b2593390c610e76ca8028ef3d24680000619ffb646276", | |
| "tokenizer_class": "T5Tokenizer", | |
| "truncation_side": "right", | |
| "unk_token": "<unk>" | |
| } | |