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Charlie81
/
SkipMoE

Text Generation
Transformers
Safetensors
English
kvlatent
Mixture of Experts
olmo
olmoe
Model card Files Files and versions
xet
Community

Instructions to use Charlie81/SkipMoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Charlie81/SkipMoE with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Charlie81/SkipMoE")
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("Charlie81/SkipMoE", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Charlie81/SkipMoE with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Charlie81/SkipMoE"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Charlie81/SkipMoE",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Charlie81/SkipMoE
  • SGLang

    How to use Charlie81/SkipMoE 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 "Charlie81/SkipMoE" \
        --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": "Charlie81/SkipMoE",
    		"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 "Charlie81/SkipMoE" \
            --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": "Charlie81/SkipMoE",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Charlie81/SkipMoE with Docker Model Runner:

    docker model run hf.co/Charlie81/SkipMoE
SkipMoE
2.67 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 4 commits
chengyanwu
stuff
ccda2ec 12 months ago
  • __pycache__
    stuff 12 months ago
  • .gitattributes
    2.14 kB
    OLMoE content added 12 months ago
  • .gitignore
    19 Bytes
    stuff 12 months ago
  • README.md
    3.32 kB
    stuff 12 months ago
  • config.json
    631 Bytes
    stuff 12 months ago
  • generate.py
    2.85 kB
    stuff 12 months ago
  • generation_config.json
    120 Bytes
    OLMoE content added 12 months ago
  • model.safetensors.index.json
    287 kB
    OLMoE content added 12 months ago
  • modeling_olmoe.py
    33.1 kB
    stuff 12 months ago
  • oldcmds.txt
    142 Bytes
    stuff 12 months ago
  • output.txt
    0 Bytes
    stuff 12 months ago
  • randommoe.py
    45.9 kB
    stuff 12 months ago
  • requirements.txt
    265 Bytes
    stuff 12 months ago
  • shellcommands.txt
    48 Bytes
    stuff 12 months ago
  • special_tokens_map.json
    65 Bytes
    OLMoE content added 12 months ago
  • tokenizer.json
    2.12 MB
    OLMoE content added 12 months ago
  • tokenizer_config.json
    5.37 kB
    OLMoE content added 12 months ago
  • train.py
    3.7 kB
    stuff 12 months ago
  • train_olmoe_adapter.py
    13.6 kB
    stuff 12 months ago