Tree: Update config_sample
Uses the new YAML generator. Signed-off-by: kingbri <bdashore3@proton.me>
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kingbri
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1 changed files with 148 additions and 164 deletions
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@ -1,5 +1,6 @@
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# Sample YAML file for configuration.
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# Comment and uncomment values as needed. Every value has a default within the application.
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# Comment and uncomment values as needed.
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# Every value has a default within the application.
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# This file serves to be a drop in for config.yml
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# Unless specified in the comments, DO NOT put these options in quotes!
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@ -8,225 +9,208 @@
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# Options for networking
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network:
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# The IP to host on (default: 127.0.0.1).
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# Use 0.0.0.0 to expose on all network adapters
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# Use 0.0.0.0 to expose on all network adapters.
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host: 127.0.0.1
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# The port to host on (default: 5000)
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# The port to host on (default: 5000).
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port: 5000
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# Disable HTTP token authenticaion with requests
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# Disable HTTP token authentication with requests.
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# WARNING: This will make your instance vulnerable!
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# Turn on this option if you are ONLY connecting from localhost
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# Turn on this option if you are ONLY connecting from localhost.
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disable_auth: False
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# Send tracebacks over the API to clients (default: False)
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# NOTE: Only enable this for debug purposes
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# Send tracebacks over the API (default: False).
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# NOTE: Only enable this for debug purposes.
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send_tracebacks: False
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# Select API servers to enable (default: ["OAI"])
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# Possible values: OAI
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api_servers: ["OAI"]
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# Select API servers to enable (default: ["OAI"]).
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# Possible values: OAI, Kobold.
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api_servers: []
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# Options for logging
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logging:
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# Enable prompt logging (default: False)
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prompt: False
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# Enable prompt logging (default: False).
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log_prompt: False
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# Enable generation parameter logging (default: False)
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generation_params: False
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# Enable generation parameter logging (default: False).
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log_generation_params: False
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# Enable request logging (default: False)
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# Enable request logging (default: False).
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# NOTE: Only use this for debugging!
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requests: False
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# Options for sampling
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sampling:
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# Override preset name. Find this in the sampler-overrides folder (default: None)
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# This overrides default fallbacks for sampler values that are passed to the API
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# Server-side overrides are NOT needed by default
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# WARNING: Using this can result in a generation speed penalty
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#override_preset:
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# Options for development and experimentation
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developer:
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# Skips exllamav2 version check (default: False)
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# It's highly recommended to update your dependencies rather than enabling this flag
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# WARNING: Don't set this unless you know what you're doing!
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#unsafe_launch: False
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# Disable all request streaming (default: False)
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# A kill switch for turning off SSE in the API server
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#disable_request_streaming: False
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# Enable the torch CUDA malloc backend (default: False)
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# This can save a few MBs of VRAM, but has a risk of errors. Use at your own risk.
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#cuda_malloc_backend: False
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# Enable Uvloop or Winloop (default: False)
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# Make the program utilize a faster async event loop which can improve performance
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# NOTE: It's recommended to enable this, but if something breaks, turn this off.
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#uvloop: False
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# Set process to use a higher priority
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# For realtime process priority, run as administrator or sudo
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# Otherwise, the priority will be set to high
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#realtime_process_priority: False
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log_requests: False
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# Options for model overrides and loading
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# Please read the comments to understand how arguments are handled between initial and API loads
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# Please read the comments to understand how arguments are handled
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# between initial and API loads
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model:
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# Overrides the directory to look for models (default: models)
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# Windows users, DO NOT put this path in quotes! This directory will be invalid otherwise.
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# Directory to look for models (default: models).
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# Windows users, do NOT put this path in quotes!
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model_dir: models
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# Sends dummy model names when the models endpoint is queried
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# Enable this if the program is looking for a specific OAI model
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#use_dummy_models: False
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# Allow direct loading of models from a completion or chat completion request
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# Allow direct loading of models from a completion or chat completion request (default: False).
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inline_model_loading: False
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# An initial model to load. Make sure the model is located in the model directory!
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# A model can be loaded later via the API.
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# REQUIRED: This must be filled out to load a model on startup!
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model_name:
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# Sends dummy model names when the models endpoint is queried.
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# Enable this if the client is looking for specific OAI models.
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use_dummy_models: False
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# The below parameters only apply for initial loads
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# All API based loads do NOT inherit these settings unless specified in use_as_default
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# An initial model to load.
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# Make sure the model is located in the model directory!
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# REQUIRED: This must be filled out to load a model on startup.
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model_name:
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# Names of args to use as a default fallback for API load requests (default: [])
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# For example, if you always want cache_mode to be Q4 instead of on the inital model load,
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# Add "cache_mode" to this array
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# Ex. ["max_seq_len", "cache_mode"]
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#use_as_default: []
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# Names of args to use as a fallback for API load requests (default: []).
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# For example, if you always want cache_mode to be Q4 instead of on the inital model load, add "cache_mode" to this array.
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# Example: ['max_seq_len', 'cache_mode'].
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use_as_default: []
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# The below parameters apply only if model_name is set
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# Max sequence length (default: Empty).
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# Fetched from the model's base sequence length in config.json by default.
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max_seq_len:
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# Max sequence length (default: Empty)
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# Fetched from the model's base sequence length in config.json by default
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#max_seq_len:
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# Overrides base model context length (default: Empty)
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# Overrides base model context length (default: Empty).
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# WARNING: Don't set this unless you know what you're doing!
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# Again, do NOT use this for configuring context length, use max_seq_len above ^
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# Only use this if the model's base sequence length in config.json is incorrect (ex. Mistral 7B)
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#override_base_seq_len:
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override_base_seq_len:
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# Load model with tensor parallelism
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# If a GPU split isn't provided, the TP loader will fallback to autosplit
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# Enabling ignores the gpu_split_auto and autosplit_reserve values
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#tensor_parallel: False
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# Load model with tensor parallelism.
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# Falls back to autosplit if GPU split isn't provided.
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# This ignores the gpu_split_auto value.
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tensor_parallel: False
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# Automatically allocate resources to GPUs (default: True)
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# NOTE: Not parsed for single GPU users
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#gpu_split_auto: True
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# Automatically allocate resources to GPUs (default: True).
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# Not parsed for single GPU users.
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gpu_split_auto: True
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# Reserve VRAM used for autosplit loading (default: 96 MB on GPU 0)
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# This is represented as an array of MB per GPU used
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#autosplit_reserve: [96]
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# Reserve VRAM used for autosplit loading (default: 96 MB on GPU 0).
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# Represented as an array of MB per GPU.
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autosplit_reserve: [96]
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# An integer array of GBs of vram to split between GPUs (default: [])
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# Used with tensor parallelism
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# NOTE: Not parsed for single GPU users
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#gpu_split: [20.6, 24]
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# An integer array of GBs of VRAM to split between GPUs (default: []).
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# Used with tensor parallelism.
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gpu_split: []
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# Rope scale (default: 1.0)
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# Same thing as compress_pos_emb
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# Only use if your model was trained on long context with rope (check config.json)
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# Leave blank to pull the value from the model
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#rope_scale: 1.0
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# Rope scale (default: 1.0).
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# Same as compress_pos_emb.
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# Use if the model was trained on long context with rope.
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# Leave blank to pull the value from the model.
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rope_scale: 1.0
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# Rope alpha (default: 1.0)
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# Same thing as alpha_value
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# Set to "auto" to automatically calculate
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# Leave blank to pull the value from the model
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#rope_alpha: 1.0
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# Rope alpha (default: 1.0).
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# Same as alpha_value. Set to "auto" to auto-calculate.
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rope_alpha: 1.0
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# Enable different cache modes for VRAM savings (slight performance hit).
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# Possible values FP16, Q8, Q6, Q4. (default: FP16)
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#cache_mode: FP16
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# Enable different cache modes for VRAM savings (default: FP16).
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# Possible values: 'FP16', 'Q8', 'Q6', 'Q4'.
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cache_mode: FP16
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# Size of the prompt cache to allocate (default: max_seq_len)
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# This must be a multiple of 256. A larger cache uses more VRAM, but allows for more prompts to be processed at once.
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# NOTE: Cache size should not be less than max_seq_len.
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# For CFG, set this to 2 * max_seq_len to make room for both positive and negative prompts.
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#cache_size:
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# Size of the prompt cache to allocate (default: max_seq_len).
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# Must be a multiple of 256 and can't be less than max_seq_len.
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# For CFG, set this to 2 * max_seq_len.
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cache_size:
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# Chunk size for prompt ingestion. A lower value reduces VRAM usage at the cost of ingestion speed (default: 2048)
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# NOTE: Effects vary depending on the model. An ideal value is between 512 and 4096
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#chunk_size: 2048
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# Chunk size for prompt ingestion (default: 2048).
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# A lower value reduces VRAM usage but decreases ingestion speed.
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# NOTE: Effects vary depending on the model.
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# An ideal value is between 512 and 4096.
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chunk_size: 2048
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# Set the maximum amount of prompts to process at one time (default: None/Automatic)
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# This will be automatically calculated if left blank.
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# A max batch size of 1 processes prompts one at a time.
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# NOTE: Only available for Nvidia ampere (30 series) and above GPUs
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#max_batch_size:
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# Set the maximum number of prompts to process at one time (default: None/Automatic).
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# Automatically calculated if left blank.
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# NOTE: Only available for Nvidia ampere (30 series) and above GPUs.
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max_batch_size:
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# Set the prompt template for this model. If empty, attempts to look for the model's chat template. (default: None)
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# If a model contains multiple templates in its tokenizer_config.json, set prompt_template to the name
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# of the template you want to use.
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# Set the prompt template for this model. (default: None)
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# If empty, attempts to look for the model's chat template.
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# If a model contains multiple templates in its tokenizer_config.json,
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# set prompt_template to the name of the template you want to use.
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# NOTE: Only works with chat completion message lists!
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#prompt_template:
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prompt_template:
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# Number of experts to use PER TOKEN. Fetched from the model's config.json if not specified (default: Empty)
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# Number of experts to use per token.
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# Fetched from the model's config.json if empty.
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# NOTE: For MoE models only.
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# WARNING: Don't set this unless you know what you're doing!
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# NOTE: For MoE models (ex. Mixtral) only!
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#num_experts_per_token:
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num_experts_per_token:
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# Enables fasttensors to possibly increase model loading speeds (default: False)
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#fasttensors: true
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# Enables fasttensors to possibly increase model loading speeds (default: False).
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fasttensors: False
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# Options for draft models (speculative decoding). This will use more VRAM!
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#draft:
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# Overrides the directory to look for draft (default: models)
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#draft_model_dir: models
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# Options for draft models (speculative decoding)
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# This will use more VRAM!
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draft_model:
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# Directory to look for draft models (default: models)
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draft_model_dir: models
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# An initial draft model to load. Make sure this model is located in the model directory!
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# A draft model can be loaded later via the API.
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#draft_model_name: A model name
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# The below parameters only apply for initial loads
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# All API based loads do NOT inherit these settings unless specified in use_as_default
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# An initial draft model to load.
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# Ensure the model is in the model directory.
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draft_model_name:
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# Rope scale for draft models (default: 1.0)
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# Same thing as compress_pos_emb
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# Only use if your draft model was trained on long context with rope (check config.json)
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#draft_rope_scale: 1.0
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# Rope scale for draft models (default: 1.0).
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# Same as compress_pos_emb.
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# Use if the draft model was trained on long context with rope.
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draft_rope_scale: 1.0
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# Rope alpha for draft model (default: 1.0)
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# Same thing as alpha_value
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# Leave blank to automatically calculate alpha value
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#draft_rope_alpha: 1.0
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# Rope alpha for draft models (default: None).
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# Same as alpha_value. Set to "auto" to auto-calculate.
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draft_rope_alpha:
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# Enable different draft model cache modes for VRAM savings (slight performance hit).
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# Possible values FP16, Q8, Q6, Q4. (default: FP16)
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#draft_cache_mode: FP16
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# Options for loras
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#lora:
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# Overrides the directory to look for loras (default: loras)
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#lora_dir: loras
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# List of loras to load and associated scaling factors (default: 1.0). Comment out unused entries or add more rows as needed.
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#loras:
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#- name: lora1
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# scaling: 1.0
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# Cache mode for draft models to save VRAM (default: FP16).
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# Possible values: 'FP16', 'Q8', 'Q6', 'Q4'.
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draft_cache_mode: FP16
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# Options for Loras
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lora:
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# Directory to look for LoRAs (default: loras).
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lora_dir: loras
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# List of LoRAs to load and associated scaling factors (default scale: 1.0).
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# For the YAML file, add each entry as a YAML list:
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# - name: lora1
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# scaling: 1.0
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loras:
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# Options for embedding models and loading.
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# NOTE: Embeddings requires the "extras" feature to be installed
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# Install it via "pip install .[extras]"
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embeddings:
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# Overrides directory to look for embedding models (default: models)
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# Directory to look for embedding models (default: models).
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embedding_model_dir: models
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# Device to load embedding models on (default: cpu)
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# Possible values: cpu, auto, cuda
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# Device to load embedding models on (default: cpu).
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# Possible values: cpu, auto, cuda.
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# NOTE: It's recommended to load embedding models on the CPU.
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# If you'd like to load on an AMD gpu, set this value to "cuda" as well.
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# If using an AMD GPU, set this value to 'cuda'.
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embeddings_device: cpu
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# The below parameters only apply for initial loads
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# All API based loads do NOT inherit these settings unless specified in use_as_default
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# An initial embedding model to load on the infinity backend.
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embedding_model_name:
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# An initial embedding model to load on the infinity backend (default: None)
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embedding_model_name:
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# Options for Sampling
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sampling:
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# Select a sampler override preset (default: None).
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# Find this in the sampler-overrides folder.
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# This overrides default fallbacks for sampler values that are passed to the API.
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override_preset:
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# Options for development and experimentation
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developer:
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# Skip Exllamav2 version check (default: False).
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# WARNING: It's highly recommended to update your dependencies rather than enabling this flag.
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unsafe_launch: False
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# Disable API request streaming (default: False).
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disable_request_streaming: False
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# Enable the torch CUDA malloc backend (default: False).
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cuda_malloc_backend: False
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# Run asyncio using Uvloop or Winloop which can improve performance.
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# NOTE: It's recommended to enable this, but if something breaks turn this off.
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uvloop: False
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# Set process to use a higher priority.
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# For realtime process priority, run as administrator or sudo.
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# Otherwise, the priority will be set to high.
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realtime_process_priority: False
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