Instructions to use Crystalcareai/GemMoE-Base-Random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Crystalcareai/GemMoE-Base-Random with Transformers:

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

pipe = pipeline("text-generation", model="Crystalcareai/GemMoE-Base-Random", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Crystalcareai/GemMoE-Base-Random", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Crystalcareai/GemMoE-Base-Random with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Crystalcareai/GemMoE-Base-Random"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Crystalcareai/GemMoE-Base-Random",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Crystalcareai/GemMoE-Base-Random

SGLang

How to use Crystalcareai/GemMoE-Base-Random 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 "Crystalcareai/GemMoE-Base-Random" \
    --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": "Crystalcareai/GemMoE-Base-Random",
		"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 "Crystalcareai/GemMoE-Base-Random" \
        --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": "Crystalcareai/GemMoE-Base-Random",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Crystalcareai/GemMoE-Base-Random with Docker Model Runner:
```
docker model run hf.co/Crystalcareai/GemMoE-Base-Random
```

Crystalcareai commited on Mar 15, 2024

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@@ -15,7 +15,7 @@ license_link: https://ai.google.dev/gemma/terms
 > **Note:** If you wish to use GemMoE while it is in beta, you must add the flag `trust_remote_code=True` in your training/inference config.
-Please read the [howto.md](https://huggingface.co/Crystalcareai/GemMoE-Base-Random/blob/main/howto.md). below for a description of these newer models.
 ## Creating Your Own GemMoE Model

 > **Note:** If you wish to use GemMoE while it is in beta, you must add the flag `trust_remote_code=True` in your training/inference config.
+Please read the [howto.md](https://huggingface.co/Crystalcareai/GemMoE-Base-Random/blob/main/howto.md) for a description of these newer models.
 ## Creating Your Own GemMoE Model