Instructions to use inceptionai/jais-13b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inceptionai/jais-13b-chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="inceptionai/jais-13b-chat", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("inceptionai/jais-13b-chat", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use inceptionai/jais-13b-chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "inceptionai/jais-13b-chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inceptionai/jais-13b-chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/inceptionai/jais-13b-chat
- SGLang
How to use inceptionai/jais-13b-chat 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 "inceptionai/jais-13b-chat" \ --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": "inceptionai/jais-13b-chat", "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 "inceptionai/jais-13b-chat" \ --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": "inceptionai/jais-13b-chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use inceptionai/jais-13b-chat with Docker Model Runner:
docker model run hf.co/inceptionai/jais-13b-chat
inference-endpoints
this will allow deployment on huggingface inference endpoint infrastructure
Thanks for making the model available for deployment!
I have added my own inference endpoint using your forked repo, but when I type a prompt in the "Test your endpoint!" text box, I get an error:
API Implementation Error: Invalid output: output must be of type <conversation: <generated_responses:Array; past_user_inputs:Array>>
Any idea what I'm doing wrong?
Thanks!
Much appreciated if you could provide inference. We would like to evaluate the model for our use cases, would be happy to share our insights too!
Thanks for making the model available for deployment!
I have added my own inference endpoint using your forked repo, but when I type a prompt in the "Test your endpoint!" text box, I get an error:
API Implementation Error: Invalid output: output must be of type <conversation: <generated_responses:Array; past_user_inputs:Array>>Any idea what I'm doing wrong?
Thanks!
Hi @infojunkie
once you have it deployed - use curl or python to call the api
i have added some desc here
https://huggingface.co/poiccard/jais-13b-chat-adn