Instructions to use apple/OpenELM-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/OpenELM-3B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="apple/OpenELM-3B-Instruct", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("apple/OpenELM-3B-Instruct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use apple/OpenELM-3B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "apple/OpenELM-3B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "apple/OpenELM-3B-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/apple/OpenELM-3B-Instruct
- SGLang
How to use apple/OpenELM-3B-Instruct 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 "apple/OpenELM-3B-Instruct" \ --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": "apple/OpenELM-3B-Instruct", "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 "apple/OpenELM-3B-Instruct" \ --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": "apple/OpenELM-3B-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use apple/OpenELM-3B-Instruct with Docker Model Runner:
docker model run hf.co/apple/OpenELM-3B-Instruct
Not able to have an output with a smaller size than the given `max_length`
Hello, when using the 3B model, I am not able to have an output with a smaller size than the given max_length.
It always give a correct answer on first tokens, and then output garbage to fill until max_length is reached.
I use the code in the generate_openelm.py as template.
I tried to adapt the different generation options as described here, like length penalty or changing the generation strategy, but without success.
Is it possible ? Any advice would be very welcome
It seems that apple does not release its chat template, so currently it only worked in text-generation (by using llama2's tokenizer)