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shafire
/
MicroSpectraMind

Text Generation
Transformers
TensorBoard
Safetensors
PEFT
Trained with AutoTrain
text-generation-inference
conversational
Model card Files Files and versions
xet
Metrics Training metrics Community

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

  • Libraries
  • Transformers

    How to use shafire/MicroSpectraMind with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="shafire/MicroSpectraMind")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("shafire/MicroSpectraMind", dtype="auto")
  • PEFT

    How to use shafire/MicroSpectraMind with PEFT:

    Task type is invalid.
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use shafire/MicroSpectraMind with vLLM:

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

    How to use shafire/MicroSpectraMind 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 "shafire/MicroSpectraMind" \
        --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": "shafire/MicroSpectraMind",
    		"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 "shafire/MicroSpectraMind" \
            --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": "shafire/MicroSpectraMind",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use shafire/MicroSpectraMind with Docker Model Runner:

    docker model run hf.co/shafire/MicroSpectraMind
MicroSpectraMind
62.4 MB
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History: 28 commits
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  • training_args.bin

    Detected Pickle imports (9)

    • "torch.device",
    • "transformers.trainer_utils.IntervalStrategy",
    • "transformers.trainer_utils.HubStrategy",
    • "trl.trainer.sft_config.SFTConfig",
    • "transformers.trainer_pt_utils.AcceleratorConfig",
    • "accelerate.state.PartialState",
    • "accelerate.utils.dataclasses.DistributedType",
    • "transformers.training_args.OptimizerNames",
    • "transformers.trainer_utils.SchedulerType"

    How to fix it?

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  • training_params.json
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