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

a718c05

1 Parent(s): 234a12a

link added

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -22,9 +22,9 @@ library_name: transformers
 - With **only 6.6B** activate parameters, GRIN MoE achieves **exceptionally good** performance across a diverse set of tasks, particularly in coding and mathematics tasks.
-- GRIN uses **SparseMixer-v2** to estimate the gradient related to expert routing, while the conventional MoE training treats expert gating as a proxy for the gradient estimation.
-- GRIN scales MoE training with **neither expert parallelism nor token dropping**, while the conventional MoE training employs expert parallelism and deploys token dropping.
 ## Intended Uses

 - With **only 6.6B** activate parameters, GRIN MoE achieves **exceptionally good** performance across a diverse set of tasks, particularly in coding and mathematics tasks.
+- GRIN uses [**SparseMixer-v2**](https://arxiv.org/html/2409.12136v1#Pt1) to estimate the gradient related to expert routing, while the conventional MoE training treats expert gating as a proxy for the gradient estimation.
+- GRIN scales MoE training with [**neither expert parallelism nor token dropping**](https://arxiv.org/pdf/2409.12136#page=5.42), while the conventional MoE training employs expert parallelism and deploys token dropping.
 ## Intended Uses