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
- 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
Any plans to release the evaluation data or evaluate AraGPT2 1.5B ?
the evaluation dataset can serve as a good benchmark for the evaluation of future Arabic LLMs.
Also are you planning to evaluate AraGPT2 1.5B? was the grover modeling file causing issues?
may i ask u please if u already installed and ran the model how to do so, cause am facing issues and dontknow how to handle?
@wissamantoun
@MazenSiraj why ? what was the issue or the error?
did you try the code snippet provided, I tried it and worked perfectly.
Can you share your problem in different post so we can help
Hello @sherif1 , it was the offload folder path issue and I managed to handle it earlier.
can you tell me how did you run the model because every time I run the sample code it takes so long to run and downloads the model all over again.
after downloading it for the first time and storing it say in a variable called model and tokenizer , save the mode and tokenizer using
model.save_pretrained(<ur path>)
tokenizer.save_pretrained(<ur path>)
The next time, don't use AutoTokenizer.from_pretrained('inception-mbzuai/jais-13b-chat')
Use ur local path like this AutoTokenizer.from_pretrained(<ur path>)
