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Wonder-Griffin
/
ZeusMM

Text Generation
Transformers
Safetensors
English
zeusmm
multimodal
chat
vision
audio
retrieval
text-generation-inference
custom_code
Model card Files Files and versions
xet
Community
1

Instructions to use Wonder-Griffin/ZeusMM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Wonder-Griffin/ZeusMM with Transformers:

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

    How to use Wonder-Griffin/ZeusMM with vLLM:

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

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

    How to use Wonder-Griffin/ZeusMM with Docker Model Runner:

    docker model run hf.co/Wonder-Griffin/ZeusMM
ZeusMM
412 MB
Ctrl+K
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  • 2 contributors
History: 9 commits
Wonder-Griffin's picture
Wonder-Griffin
Update README.md
c2848f3 verified 9 months ago
  • .gitattributes
    1.52 kB
    initial commit 9 months ago
  • README.md
    5.77 kB
    Update README.md 9 months ago
  • added_tokens.json
    222 Bytes
    Initial ZeusMM: tiny-pretrained weights, tokenizer, config, custom code 9 months ago
  • config.json
    911 Bytes
    Re-save weights in safetensors 9 months ago
  • generation_config.json
    73 Bytes
    Re-save weights in safetensors 9 months ago
  • merges.txt
    456 kB
    Initial ZeusMM: tiny-pretrained weights, tokenizer, config, custom code 9 months ago
  • model.safetensors
    407 MB
    xet
    Adding `safetensors` variant of this model (#1) 9 months ago
  • special_tokens_map.json
    819 Bytes
    Initial ZeusMM: tiny-pretrained weights, tokenizer, config, custom code 9 months ago
  • tokenizer.json
    3.56 MB
    Re-save weights in safetensors 9 months ago
  • tokenizer_config.json
    2.52 kB
    Initial ZeusMM: tiny-pretrained weights, tokenizer, config, custom code 9 months ago
  • vocab.json
    798 kB
    Initial ZeusMM: tiny-pretrained weights, tokenizer, config, custom code 9 months ago
  • zeus_mm.py
    24.8 kB
    Initial ZeusMM: tiny-pretrained weights, tokenizer, config, custom code 9 months ago