# Sample YAML file for configuration.
# Comment and uncomment values as needed.
# Every value has a default within the application.
# This file serves to be 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: 5000

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

  # Send tracebacks over the API (default: False).
  # NOTE: Only enable this for debug purposes.
  send_tracebacks: false

  # Select API servers to enable (default: ["OAI"]).
  # Possible values: OAI, Kobold.
  api_servers: ["oai"]

# Options for logging
logging:
  # Enable prompt logging (default: False).
  log_prompt: false

  # Enable generation parameter logging (default: False).
  log_generation_params: false

  # Enable request logging (default: False).
  # NOTE: Only use this for debugging!
  log_requests: false

# Options for model overrides and loading
# Please read the comments to understand how arguments are handled
# between initial and API loads
model:
  # Directory to look for models (default: models).
  # Windows users, do NOT put this path in quotes!
  model_dir: models

  # Allow direct loading of models from a completion or chat completion request (default: False).
  inline_model_loading: false

  # Sends dummy model names when the models endpoint is queried.
  # Enable this if the client is looking for specific OAI models.
  use_dummy_models: false

  # An initial model to load.
  # Make sure the model is located in the model directory!
  # REQUIRED: This must be filled out to load a model on startup.
  model_name: Llama-3.1-Nemotron-70B-Instruct-HF_exl2_4.6bpw

  # Names of args to use as a fallback for API load requests (default: []).
  # For example, if you always want cache_mode to be Q4 instead of on the inital model load, add "cache_mode" to this array.
  # Example: ['max_seq_len', 'cache_mode'].
  use_as_default: []

  # Max sequence length (default: Empty).
  # Fetched from the model's base sequence length in config.json by default.
  max_seq_len: 65536
  # Overrides base model context length (default: Empty).
  # WARNING: Don't set this unless you know what you're doing!
  # Again, do NOT use this for configuring context length, use max_seq_len above ^
  override_base_seq_len:

  # Load model with tensor parallelism.
  # Falls back to autosplit if GPU split isn't provided.
  # This ignores the gpu_split_auto value.
  tensor_parallel: false

  # Automatically allocate resources to GPUs (default: True).
  # Not parsed for single GPU users.
  gpu_split_auto: true

  # Reserve VRAM used for autosplit loading (default: 96 MB on GPU 0).
  # Represented as an array of MB per GPU.
  autosplit_reserve: [0]

  # An integer array of GBs of VRAM to split between GPUs (default: []).
  # Used with tensor parallelism.
  gpu_split: []

  # Rope scale (default: 1.0).
  # Same as compress_pos_emb.
  # Use if the model was trained on long context with rope.
  # Leave blank to pull the value from the model.
  rope_scale: 1.0

  # Rope alpha (default: None).
  # Same as alpha_value. Set to "auto" to auto-calculate.
  # Leaving this value blank will either pull from the model or auto-calculate.
  rope_alpha:

  # Enable different cache modes for VRAM savings (default: FP16).
  # Possible values: 'FP16', 'Q8', 'Q6', 'Q4'.
  cache_mode: Q4

  # Size of the prompt cache to allocate (default: max_seq_len).
  # Must be a multiple of 256 and can't be less than max_seq_len.
  # For CFG, set this to 2 * max_seq_len.
  cache_size:

  # Chunk size for prompt ingestion (default: 2048).
  # A lower value reduces VRAM usage but decreases ingestion speed.
  # NOTE: Effects vary depending on the model.
  # An ideal value is between 512 and 4096.
  chunk_size: 2048

  # Set the maximum number of prompts to process at one time (default: None/Automatic).
  # Automatically calculated if left blank.
  # NOTE: Only available for Nvidia ampere (30 series) and above GPUs.
  max_batch_size:

  # Set the prompt template for this model. (default: None)
  # If empty, attempts to look for the model's chat template.
  # If a model contains multiple templates in its tokenizer_config.json,
  # set prompt_template to the name of the template you want to use.
  # 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 empty.
  # NOTE: For MoE models only.
  # WARNING: Don't set this unless you know what you're doing!
  num_experts_per_token:

  # Enables fasttensors to possibly increase model loading speeds (default: False).
  fasttensors: true

# Options for draft models (speculative decoding)
# This will use more VRAM!
draft_model:
  # Directory to look for draft models (default: models)
  draft_model_dir: models

  # An initial draft model to load.
  # Ensure the model is in the model directory.
  draft_model_name:

  # Rope scale for draft models (default: 1.0).
  # Same as compress_pos_emb.
  # Use if the draft model was trained on long context with rope.
  draft_rope_scale: 1.0

  # Rope alpha for draft models (default: None).
  # Same as alpha_value. Set to "auto" to auto-calculate.
  # Leaving this value blank will either pull from the model or auto-calculate.
  draft_rope_alpha:

  # Cache mode for draft models to save VRAM (default: FP16).
  # Possible values: 'FP16', 'Q8', 'Q6', 'Q4'.
  draft_cache_mode: FP16

# Options for Loras
lora:
  # Directory to look for LoRAs (default: loras).
  lora_dir: loras

  # List of LoRAs to load and associated scaling factors (default scale: 1.0).
  # For the YAML file, add each entry as a YAML list:
  # - name: lora1
  #   scaling: 1.0
  loras:

# Options for embedding models and loading.
# NOTE: Embeddings requires the "extras" feature to be installed
# Install it via "pip install .[extras]"
embeddings:
  # Directory to look for embedding models (default: models).
  embedding_model_dir: models

  # Device to load embedding models on (default: cpu).
  # Possible values: cpu, auto, cuda.
  # NOTE: It's recommended to load embedding models on the CPU.
  # If using an AMD GPU, set this value to 'cuda'.
  embeddings_device: cpu

  # An initial embedding model to load on the infinity backend.
  embedding_model_name:
sampling:

# Options for development and experimentation
developer:
  # Skip Exllamav2 version check (default: False).
  # WARNING: It's highly recommended to update your dependencies rather than enabling this flag.
  unsafe_launch: false

  # Disable API request streaming (default: False).
  disable_request_streaming: false

  # Enable the torch CUDA malloc backend (default: False).
  cuda_malloc_backend: true

  # Run asyncio using Uvloop or Winloop which can improve performance.
  # NOTE: It's recommended to enable this, but if something breaks turn this off.
  uvloop: true

  # Set process to use a higher priority.
  # For realtime process priority, run as administrator or sudo.
  # Otherwise, the priority will be set to high.
  realtime_process_priority: true