Instructions to use Uni-MoE/Uni-MoE-8e-e2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Uni-MoE/Uni-MoE-8e-e2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Uni-MoE/Uni-MoE-8e-e2")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Uni-MoE/Uni-MoE-8e-e2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Uni-MoE/Uni-MoE-8e-e2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Uni-MoE/Uni-MoE-8e-e2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Uni-MoE/Uni-MoE-8e-e2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Uni-MoE/Uni-MoE-8e-e2
- SGLang
How to use Uni-MoE/Uni-MoE-8e-e2 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 "Uni-MoE/Uni-MoE-8e-e2" \ --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": "Uni-MoE/Uni-MoE-8e-e2", "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 "Uni-MoE/Uni-MoE-8e-e2" \ --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": "Uni-MoE/Uni-MoE-8e-e2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Uni-MoE/Uni-MoE-8e-e2 with Docker Model Runner:
docker model run hf.co/Uni-MoE/Uni-MoE-8e-e2
Download mlp_gates.bin from Uni-MoE/Uni-MoE-8e-e2: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/Uni-MoE/Uni-MoE-8e-e2/resolve/main/mlp_gates.bin
- Command line
-
hf download hf://Uni-MoE/Uni-MoE-8e-e2/mlp_gates.bin
-
curl -L -o mlp_gates.bin https://huggingface.co/Uni-MoE/Uni-MoE-8e-e2/resolve/main/mlp_gates.bin
2.11 MB
- Xet hash:
- c3dc58bdaf5ae28c6b1526004e5b36722dac354f5975da582079ccad8c08f9d1
- Size of remote file:
- 2.11 MB
- SHA256:
- 6fb75bb523ebc130e57fc350f0fc14cc6698c2a19f69753b40d703204aa521b2
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