Instructions to use BeardedMonster/MOE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BeardedMonster/MOE with Transformers:

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

pipe = pipeline("text-generation", model="BeardedMonster/MOE", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("BeardedMonster/MOE", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BeardedMonster/MOE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BeardedMonster/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": "BeardedMonster/MOE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BeardedMonster/MOE

SGLang

How to use BeardedMonster/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 "BeardedMonster/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": "BeardedMonster/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 "BeardedMonster/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": "BeardedMonster/MOE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BeardedMonster/MOE with Docker Model Runner:
```
docker model run hf.co/BeardedMonster/MOE
```

MOE / config.json

BeardedMonster

Upload GPTJXMoEForCausalLM

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	{
	"_name_or_path": "BeardedMonster/MOE",
	"architectures": [
	"GPTJXMoEForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration.GPTJXMoEConfig",
	"AutoModelForCausalLM": "modeling.GPTJXMoEForCausalLM"
	},
	"bias": false,
	"block_size": 32768,
	"dropout": 0.0,
	"kv_cache_dtype": "float32",
	"max_batch_size": 1,
	"model_type": "sabiyarn",
	"moe_dim": 3072,
	"n_embd": 768,
	"n_heads": 12,
	"n_layer": 12,
	"num_experts": 7,
	"num_experts_per_tok": 2,
	"torch_dtype": "float32",
	"transformers_version": "4.41.2",
	"use_kv_cache": true,
	"use_moe": true,
	"vocab_size": 52050
	}