# Triton backend to use
name: "Llama-3.2-1B-Instruct"
backend: "python"
max_batch_size: 4

# Triton should expect as input a single string
# input of variable length named 'text_input'
input [

  {
    name: "input0"
    data_type: TYPE_FP32
    dims: [ 1, 1, 1, 1 ]


    reshape {
      shape: [ 11, 11, 11 ]
    }

  },
  {
    name: "input1"
    data_type: TYPE_UINT64
    dims: [ 2, 2, 2, 2 ]


    reshape {
      shape: [ 22, 22, 22, 22 ]
    }

  }

]


# Triton should expect to respond with a single string
# output of variable length named 'text_output'
output [

  {
    name: "output0"
    data_type: TYPE_BOOL
    dims: [ 1 ]

  },
  {
    name: "output1"
    data_type: TYPE_FP64
    dims: [ 1, 100 ]

    reshape {
      shape: [ 100 ]
    }

  },
  {
    name: "text_output"
    data_type: TYPE_STRING
    dims: [ -1 ]

  }

]

parameters: [
  {
    key: "base_model_path",
    value: {string_value: "/cheetah/input/model/groupuser/Llama-3.2-1B-Instruct"}
  },
  {
    key: "is_adapter_model",
    value: {string_value: "false"}
  },
  {
    key: "adapter_model_path",
    value: {string_value: ""}
  },
  
  {
    key: "quantization",
    value: {string_value: "int4"}
  }
]

instance_group [
  {
    kind: KIND_GPU
    count: 1
  }
]