Efficient Large Scale Language Modeling with Mixtures of Experts
Paper • 2112.10684 • Published • 2
How to use pingzhili/fairseq-moe-15b-bf16 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="pingzhili/fairseq-moe-15b-bf16") # Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("pingzhili/fairseq-moe-15b-bf16", device_map="auto")How to use pingzhili/fairseq-moe-15b-bf16 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pingzhili/fairseq-moe-15b-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "pingzhili/fairseq-moe-15b-bf16",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/pingzhili/fairseq-moe-15b-bf16
How to use pingzhili/fairseq-moe-15b-bf16 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "pingzhili/fairseq-moe-15b-bf16" \
--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": "pingzhili/fairseq-moe-15b-bf16",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "pingzhili/fairseq-moe-15b-bf16" \
--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": "pingzhili/fairseq-moe-15b-bf16",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use pingzhili/fairseq-moe-15b-bf16 with Docker Model Runner:
docker model run hf.co/pingzhili/fairseq-moe-15b-bf16
This is a Hugging Face transformers-style conversion of the original SMoE 15B-parameter model with BFLOAT16 from the paper "Efficient Large Scale Language Modeling with Mixtures of Experts" from Artetxe et al. The original model card can be found at https://github.com/facebookresearch/fairseq/blob/main/examples/moe_lm/model_card.md.
The usage example and modeling code can be found at https://github.com/pingzhili/light-fairseq