Instructions to use apple/OpenELM-270M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use apple/OpenELM-270M-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="apple/OpenELM-270M-Instruct", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("apple/OpenELM-270M-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use apple/OpenELM-270M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "apple/OpenELM-270M-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-270M-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/apple/OpenELM-270M-Instruct

SGLang

How to use apple/OpenELM-270M-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-270M-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-270M-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-270M-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-270M-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use apple/OpenELM-270M-Instruct with Docker Model Runner:
```
docker model run hf.co/apple/OpenELM-270M-Instruct
```

qicao-apple commited on Apr 24, 2024

Commit

1096244

1 Parent(s): c401df2

add OpenELM-270M

Browse files

Files changed (1) hide show

README.md +26 -0

README.md CHANGED Viewed

@@ -161,3 +161,29 @@ lm_eval --model hf \
 ## Bias, Risks, and Limitations
 The release of OpenELM models aims to empower and enrich the open research community by providing access to state-of-the-art language models. Trained on publicly available datasets, these models are made available without any safety guarantees. Consequently, there exists the possibility of these models producing outputs that are inaccurate, harmful, biased, or objectionable in response to user prompts. Thus, it is imperative for users and developers to undertake thorough safety testing and implement appropriate filtering mechanisms tailored to their specific requirements.

 ## Bias, Risks, and Limitations
 The release of OpenELM models aims to empower and enrich the open research community by providing access to state-of-the-art language models. Trained on publicly available datasets, these models are made available without any safety guarantees. Consequently, there exists the possibility of these models producing outputs that are inaccurate, harmful, biased, or objectionable in response to user prompts. Thus, it is imperative for users and developers to undertake thorough safety testing and implement appropriate filtering mechanisms tailored to their specific requirements.
+## Citation
+If you find our work useful, please cite:
+```BibTex
+@article{mehtaOpenELMEfficientLanguage2024,
+	title = {{OpenELM}: {An} {Efficient} {Language} {Model} {Family} with {Open}-source {Training} and {Inference} {Framework}},
+	shorttitle = {{OpenELM}},
+	url = {https://arxiv.org/abs/2404.14619v1},
+	language = {en},
+	urldate = {2024-04-24},
+	journal = {arXiv.org},
+	author = {Mehta, Sachin and Sekhavat, Mohammad Hossein and Cao, Qingqing and Horton, Maxwell and Jin, Yanzi and Sun, Chenfan and Mirzadeh, Iman and Najibi, Mahyar and Belenko, Dmitry and Zatloukal, Peter and Rastegari, Mohammad},
+	month = apr,
+	year = {2024},
+}
+@inproceedings{mehta2022cvnets,
+     author = {Mehta, Sachin and Abdolhosseini, Farzad and Rastegari, Mohammad},
+     title = {CVNets: High Performance Library for Computer Vision},
+     year = {2022},
+     booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
+     series = {MM '22}
+}
+```