Instructions to use Crystalcareai/GemMoE-Medium-V0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Crystalcareai/GemMoE-Medium-V0.5 with Transformers:

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

pipe = pipeline("text-generation", model="Crystalcareai/GemMoE-Medium-V0.5", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Crystalcareai/GemMoE-Medium-V0.5", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Crystalcareai/GemMoE-Medium-V0.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Crystalcareai/GemMoE-Medium-V0.5"
# 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-Medium-V0.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Crystalcareai/GemMoE-Medium-V0.5

SGLang

How to use Crystalcareai/GemMoE-Medium-V0.5 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-Medium-V0.5" \
    --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-Medium-V0.5",
		"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-Medium-V0.5" \
        --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-Medium-V0.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Crystalcareai/GemMoE-Medium-V0.5 with Docker Model Runner:
```
docker model run hf.co/Crystalcareai/GemMoE-Medium-V0.5
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

import torch

from transformers import AutoTokenizer, TextStreamer, AutoModelForCausalLM

model_path = "Crystalcareai/GemMoE-Medium-v0.5"

# Load model

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    low_cpu_mem_usage=True,
    torch_dtype=torch.float16,
    attn_implementation="flash_attention_2",
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path)

streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

# Convert prompt to tokens

prompt_template = "[INST] {prompt} [/INST]"
prompt = "You're standing on the surface of the Earth. "\
         "You walk one mile south, one mile west and one mile north. "\
         "You end up exactly where you started. Where are you?"

tokens = tokenizer(
    prompt_template.format(prompt=prompt),
    return_tensors='pt'
).input_ids.cuda()

# Generate output

generation_output = model.generate(
    tokens,
    streamer=streamer,
    max_new_tokens=512
)

Downloads last month: 20

Safetensors

Model size

28B params

Tensor type

F16