Instructions to use allenai/StdMoE_1b14b_1T_EmoAnnealed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allenai/StdMoE_1b14b_1T_EmoAnnealed with Transformers:

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

pipe = pipeline("text-generation", model="allenai/StdMoE_1b14b_1T_EmoAnnealed", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("allenai/StdMoE_1b14b_1T_EmoAnnealed", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use allenai/StdMoE_1b14b_1T_EmoAnnealed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allenai/StdMoE_1b14b_1T_EmoAnnealed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/StdMoE_1b14b_1T_EmoAnnealed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allenai/StdMoE_1b14b_1T_EmoAnnealed

SGLang

How to use allenai/StdMoE_1b14b_1T_EmoAnnealed 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 "allenai/StdMoE_1b14b_1T_EmoAnnealed" \
    --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": "allenai/StdMoE_1b14b_1T_EmoAnnealed",
		"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 "allenai/StdMoE_1b14b_1T_EmoAnnealed" \
        --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": "allenai/StdMoE_1b14b_1T_EmoAnnealed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use allenai/StdMoE_1b14b_1T_EmoAnnealed with Docker Model Runner:
```
docker model run hf.co/allenai/StdMoE_1b14b_1T_EmoAnnealed
```

StdMoE_1b14b_1T_EmoAnnealed / model-00010-of-00011.safetensors

ryanyxw

Add model and trust_remote_code files.

2a17bf6 verified 3 months ago

Download

History Blame Contribute Delete

4.99 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 9bb74b5430ab50715923fc9c5ae4631a01c0bb90818756ee251bc59cd5101759
Size of remote file:: 4.99 GB
SHA256:: 3806c9a0cb5f7e010f1486579b9d0d996b6f5b7e11f153287410db127e940670

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.