repo_id:
  - 'THUDM/chatglm-6b'
  - 'THUDM/chatglm2-6b'
  - 'meta-llama/Llama-2-7b-chat-hf'
  # - 'liuhaotian/llava-v1.5-7b' # requires a LLAVA_REPO_DIR env variables pointing to the llava dir; added only for gpu win related test_api now
local_model_hub: 'path to your local model hub'
warm_up: 1
num_trials: 3
num_beams: 1 # default to greedy search
low_bit: 'sym_int4' # default to use 'sym_int4' (i.e. symmetric int4)
batch_size: 1 # default to 1
in_out_pairs:
  - '32-32'
  - '1024-128'
test_api:
  - "transformer_int4"
  - "native_int4"
  - "optimize_model"
  - "pytorch_autocast_bf16"
  # - "transformer_autocast_bf16"
  # - "bigdl_ipex_bf16"
  # - "bigdl_ipex_int4"
  # - "bigdl_ipex_int8"
  # - "ipex_fp16_gpu" # on Intel GPU
  # - "bigdl_fp16_gpu" # on Intel GPU
  # - "transformer_int4_gpu"  # on Intel GPU
  # - "optimize_model_gpu"  # on Intel GPU
  # - "deepspeed_transformer_int4_cpu" # on Intel SPR Server
  # - "transformer_int4_gpu_win" # on Intel GPU for Windows
  # - "transformer_int4_fp16_gpu_win" # on Intel GPU for Windows, use fp16 for non-linear layer
  # - "transformer_int4_loadlowbit_gpu_win" # on Intel GPU for Windows using load_low_bit API. Please make sure you have used the save.py to save the converted low bit model
  # - "deepspeed_optimize_model_gpu" # deepspeed autotp on Intel GPU
  # - "speculative_cpu"
  # - "speculative_gpu"
cpu_embedding: False # whether put embedding to CPU (only avaiable now for gpu win related test_api)
streaming: False # whether output in streaming way (only avaiable now for gpu win related test_api)