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

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
```

No gemmoe branch in github

by rabimba - opened Apr 19, 2024

Discussion

rabimba

Apr 19, 2024

In both colab and howto.md I see this instruction

git clone -b gemmoe https://github.com/Crystalcareai/mergekit.git

But the github repo does not seem to have that branch at all.
Am I missing something?

Crystalcareai

Owner Apr 19, 2024

Interesting. I’ll take a look when I get the chance, but stock mergekit now has native support for Gemma. You may need to go in and change the mistral.json to Gemma.json in the architecture.py file, though.

Crystalcareai

Owner Apr 19, 2024

You need to do: git clone -b fix-moe https://github.com/Crystalcareai/mergekit.git

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