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mobilint
/
HyperCLOVAX-SEED-Text-Instruct-1.5B

Text Generation
Transformers
Safetensors
Mobilint
mobilint-llama
conversational
custom_code
Model card Files Files and versions
xet
Community

Instructions to use mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B", trust_remote_code=True)
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B", trust_remote_code=True, dtype="auto")
  • Mobilint

    How to use mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B with Mobilint:

    # pip install mblt-model-zoo
    from mblt_model_zoo.vision import MBLT_Engine
    
    model = MBLT_Engine(
        model_cls="HyperCLOVAX-SEED-Text-Instruct-1.5B",
        model_type="DEFAULT",
        model_path="",
        core_mode="global8",
    )
    
    try:
        image = model.preprocess("path/to/image.jpg")
        output = model(image)
        result = model.postprocess(output)
    finally:
        model.dispose()
    
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B
  • SGLang

    How to use mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B 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 "mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    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 "mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B with Docker Model Runner:

    docker model run hf.co/mobilint/HyperCLOVAX-SEED-Text-Instruct-1.5B
HyperCLOVAX-SEED-Text-Instruct-1.5B
1.77 GB
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  • 3 contributors
History: 23 commits
lbs1163's picture
lbs1163
Update model card metadata
2761560 verified 18 days ago
  • .gitattributes
    1.56 kB
    Normalize mxq LFS patterns 3 months ago
  • HyperCLOVAX-SEED-Text-Instruct-1.5B-W4V8.mxq
    858 MB
    xet
    Upload HyperCLOVAX-SEED-Text-Instruct-1.5B-W4V8.mxq to main 3 months ago
  • README.md
    802 Bytes
    Update model card metadata 18 days ago
  • config.json
    1.14 kB
    chore: set max_batch_size to 1 about 2 months ago
  • model.safetensors
    906 MB
    xet
    Upload folder using huggingface_hub 7 months ago
  • proxy_llama.py
    478 Bytes
    Create proxy_llama.py 6 months ago
  • special_tokens_map.json
    1.93 kB
    Upload folder using huggingface_hub 7 months ago
  • tokenizer.json
    8.03 MB
    Upload folder using huggingface_hub 7 months ago
  • tokenizer_config.json
    11.9 kB
    Upload folder using huggingface_hub 7 months ago