Instructions to use microsoft/GRIN-MoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/GRIN-MoE with Transformers:

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

pipe = pipeline("text-generation", model="microsoft/GRIN-MoE", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("microsoft/GRIN-MoE", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/GRIN-MoE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/GRIN-MoE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/GRIN-MoE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/microsoft/GRIN-MoE

SGLang

How to use microsoft/GRIN-MoE 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 "microsoft/GRIN-MoE" \
    --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": "microsoft/GRIN-MoE",
		"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 "microsoft/GRIN-MoE" \
        --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": "microsoft/GRIN-MoE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use microsoft/GRIN-MoE with Docker Model Runner:
```
docker model run hf.co/microsoft/GRIN-MoE
```

LiyuanLucasLiu commited on Sep 19, 2024

Commit

c4cfe3e

1 Parent(s): 9b205aa

added arxiv link

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -17,7 +17,7 @@ library_name: transformers
 <h1 align="center">	&#128513; MoE</h1>
 <h4 align="center">GRIN: <em>GR</em>adient-<em>IN</em>formed MoE</h4>
 <p align="center">
-<a href="https://huggingface.co/microsoft/GRIN-MoE">Hugging Face</a>&nbsp | &nbsp <a href="https://huggingface.co/microsoft/GRIN-MoE/blob/main/GRIN_MoE.pdf"> Tech Report</a>&nbsp | &nbsp  <a href="https://huggingface.co/microsoft/GRIN-MoE/blob/main/LICENSE">License</a>&nbsp  | &nbsp <a href="https://github.com/microsoft/GRIN-MoE">Github</a> &nbsp | &nbsp <a href="https://huggingface.co/microsoft/GRIN-MoE#usage">Get Started</a>&nbsp
 <br>
 - With **only 6.6B** activate parameters, GRIN MoE achieves **exceptionally good** performance across a diverse set of tasks, particularly in coding and mathematics tasks.

 <h1 align="center">	&#128513; MoE</h1>
 <h4 align="center">GRIN: <em>GR</em>adient-<em>IN</em>formed MoE</h4>
 <p align="center">
+<a href="https://huggingface.co/microsoft/GRIN-MoE">Hugging Face</a>&nbsp | &nbsp <a href="https://arxiv.org/abs/2409.12136">Tech Report</a>&nbsp | &nbsp  <a href="https://huggingface.co/microsoft/GRIN-MoE/blob/main/LICENSE">License</a>&nbsp  | &nbsp <a href="https://github.com/microsoft/GRIN-MoE">Github</a> &nbsp | &nbsp <a href="https://huggingface.co/microsoft/GRIN-MoE#usage">Get Started</a>&nbsp
 <br>
 - With **only 6.6B** activate parameters, GRIN MoE achieves **exceptionally good** performance across a diverse set of tasks, particularly in coding and mathematics tasks.