Issue while running TabbyAPI on multi GPU Setup

Hi,
I am trying to run tabbyAPI on a Multi-GPU Setup ( AWS EC2 instance -g5.12xlarge). It has 4 GPUs configured. When I am trying to run using the command docker compose build, it starts the server but uses a single GPU to handle the load. This is my config.yml file
# Sample YAML file for configuration.
# Comment and uncomment values as needed. Every value has a default within the application.
# This file serves as a drop in for config.yml

# Unless specified in the comments, DO NOT put these options in quotes!
# You can use https://www.yamllint.com/ if you want to check your YAML formatting.

# Options for networking
network:
  # The IP to host on (default: 127.0.0.1).
  # Use 0.0.0.0 to expose on all network adapters
  host: 0.0.0.0

  # The port to host on (default: 5000)
  port: 8000

  # Disable HTTP token authenticaion with requests
  # WARNING: This will make your instance vulnerable!
  # Turn on this option if you are ONLY connecting from localhost
  disable_auth: True

# Options for logging
logging:
  # Enable prompt logging (default: False)
  prompt: False

  # Enable generation parameter logging (default: False)
  generation_params: True

# Options for model overrides and loading
model:
  # Overrides the directory to look for models (default: models)
  # Windows users, DO NOT put this path in quotes! This directory will be invalid otherwise.
  model_dir: models

  # An initial model to load. Make sure the model is located in the model directory!
  # A model can be loaded later via the API.
  # REQUIRED: This must be filled out to load a model on startup!
  model_name: Llama-2-7b-chat-hf

  # Enable this if the program is looking for a specific OAI model
  #use_dummy_models: False

  # The below parameters apply only if model_name is set

  # Max sequence length (default: Empty)
  # Fetched from the model's base sequence length in config.json by default
  #max_seq_len:

  # Overrides base model context length (default: Empty)
  # WARNING: Don't set this unless you know what you're doing!
  # Only use this if the model's base sequence length in config.json is incorrect (ex. Mistral/Mixtral models)
  #override_base_seq_len:

  # Automatically allocate resources to GPUs (default: True)
  gpu_split_auto: True

  # An integer array of GBs of vram to split between GPUs (default: [])
  # gpu_split: [22,22,22,22]

  # Rope scale (default: 1.0)
  # Same thing as compress_pos_emb
  # Only use if your model was trained on long context with rope (check config.json)
  # Leave blank to pull the value from the model
  #rope_scale: 1.0

  # Rope alpha (default: 1.0)
  # Same thing as alpha_value
  # Leave blank to automatically calculate alpha
  #rope_alpha: 1.0

  # Disable Flash-attention 2. Set to True for GPUs lower than Nvidia's 3000 series. (default: False)
  #no_flash_attention: False

  # Enable 8 bit cache mode for VRAM savings (slight performance hit). Possible values FP16, FP8. (default: FP16)
  # cache_mode: FP16

  # Set the prompt template for this model. If empty, chat completions will be disabled. (default: Empty)
  # NOTE: Only works with chat completion message lists!
  #prompt_template:

  # Number of experts to use PER TOKEN. Fetched from the model's config.json if not specified (default: Empty)
  # WARNING: Don't set this unless you know what you're doing!
  # NOTE: For MoE models (ex. Mixtral) only!
  #num_experts_per_token:

  # Enables CFG support (default: False)
  # WARNING: This flag disables Flash Attention! (a stopgap fix until it's fixed in upstream)
  #use_cfg: False

  # Options for draft models (speculative decoding). This will use more VRAM!
  #draft:
    # Overrides the directory to look for draft (default: models)
    #draft_model_dir: models

    # An initial draft model to load. Make sure this model is located in the model directory!
    # A draft model can be loaded later via the API.
    #draft_model_name: A model name

    # Rope scale for draft models (default: 1.0)
    # Same thing as compress_pos_emb
    # Only use if your draft model was trained on long context with rope (check config.json)
    #draft_rope_scale: 1.0

    # Rope alpha for draft model (default: 1.0)
    # Same thing as alpha_value
    # Leave blank to automatically calculate alpha value
    #draft_rope_alpha: 1.0

  # Options for loras
  #lora:
    # Overrides the directory to look for loras (default: loras)
    #lora_dir: loras

    # List of loras to load and associated scaling factors (default: 1.0). Comment out unused entries or add more rows as needed.
    #loras:
    #- name: lora1
    #  scaling: 1.0
Can someone help me with running this on multi-GPUs.
theroyallab / tabbyAPI

Issue while running TabbyAPI on multi GPU Setup #55
