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ISEKAI-Portal
/
LCL_2WAY_WEIGHT

Text Generation
Transformers
PyTorch
llava
Model card Files Files and versions
xet
Community

Instructions to use ISEKAI-Portal/LCL_2WAY_WEIGHT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ISEKAI-Portal/LCL_2WAY_WEIGHT with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="ISEKAI-Portal/LCL_2WAY_WEIGHT")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForCausalLM
    
    processor = AutoProcessor.from_pretrained("ISEKAI-Portal/LCL_2WAY_WEIGHT")
    model = AutoModelForCausalLM.from_pretrained("ISEKAI-Portal/LCL_2WAY_WEIGHT", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use ISEKAI-Portal/LCL_2WAY_WEIGHT with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "ISEKAI-Portal/LCL_2WAY_WEIGHT"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "ISEKAI-Portal/LCL_2WAY_WEIGHT",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/ISEKAI-Portal/LCL_2WAY_WEIGHT
  • SGLang

    How to use ISEKAI-Portal/LCL_2WAY_WEIGHT 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 "ISEKAI-Portal/LCL_2WAY_WEIGHT" \
        --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": "ISEKAI-Portal/LCL_2WAY_WEIGHT",
    		"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 "ISEKAI-Portal/LCL_2WAY_WEIGHT" \
            --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": "ISEKAI-Portal/LCL_2WAY_WEIGHT",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use ISEKAI-Portal/LCL_2WAY_WEIGHT with Docker Model Runner:

    docker model run hf.co/ISEKAI-Portal/LCL_2WAY_WEIGHT
LCL_2WAY_WEIGHT
27 GB
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  • 3 contributors
History: 5 commits
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weepiess2383
Create README.md
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  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • README.md
    24 Bytes
    Create README.md almost 3 years ago
  • added_tokens.json
    70 Bytes
    First Version almost 3 years ago
  • cfg.py
    3.4 kB
    First Version almost 3 years ago
  • config.json
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  • generation_config.json
    132 Bytes
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  • pytorch_model-00001-of-00003.bin
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    xet
    Upload 3 files almost 3 years ago
  • pytorch_model-00002-of-00003.bin
    9.89 GB
    xet
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  • pytorch_model-00003-of-00003.bin
    7.2 GB
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  • pytorch_model.bin.index.json
    26.9 kB
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  • special_tokens_map.json
    435 Bytes
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  • tokenizer.model
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  • tokenizer_config.json
    728 Bytes
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  • train_results.json
    168 Bytes
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  • training_args.bin
    4.28 kB
    xet
    First Version almost 3 years ago