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)

# Load model directly
from transformers import GRIN-MoE
model = GRIN-MoE.from_pretrained("microsoft/GRIN-MoE", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

rikf commited on May 23, 2025

Commit

27dffa4

verified ·

1 Parent(s): a718c05

Avoid using in-place torch operation for scatter_add

Browse files

Replace the in-place scatter add with the out of place equivalent

Files changed (1) hide show

modeling_grinmoe.py +2 -1

modeling_grinmoe.py CHANGED Viewed

@@ -786,7 +786,8 @@ class mp(torch.autograd.Function):
         grad_at_output = grad_at_output * multiplier
         grad_at_scores_expaned = masked_gates * grad_at_output.mul(-1)
-        grad_at_scores_expaned.scatter_add_(
             dim=-1,
             index=selected_experts,
             src=grad_at_output,

         grad_at_output = grad_at_output * multiplier
         grad_at_scores_expaned = masked_gates * grad_at_output.mul(-1)
+        grad_at_scores_expaned = torch.scatter_add(
+            grad_at_scores_expaned,
             dim=-1,
             index=selected_experts,
             src=grad_at_output,