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Sai Soundararaj
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4 changed files with 319 additions and 63 deletions
100
docs/_static/llama-stack-spec.yaml
vendored
100
docs/_static/llama-stack-spec.yaml
vendored
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@ -8064,10 +8064,13 @@ components:
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properties:
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job_uuid:
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type: string
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description: Unique identifier for the training job
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checkpoints:
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type: array
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items:
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$ref: '#/components/schemas/Checkpoint'
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description: >-
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List of model checkpoints created during training
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additionalProperties: false
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required:
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- job_uuid
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@ -8079,6 +8082,7 @@ components:
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properties:
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job_uuid:
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type: string
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description: Unique identifier for the training job
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status:
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type: string
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enum:
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@ -8087,16 +8091,22 @@ components:
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- failed
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- scheduled
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- cancelled
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title: JobStatus
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description: Current status of the training job
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scheduled_at:
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type: string
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format: date-time
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description: >-
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(Optional) Timestamp when the job was scheduled
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started_at:
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type: string
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format: date-time
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description: >-
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(Optional) Timestamp when the job execution began
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completed_at:
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type: string
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format: date-time
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description: >-
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(Optional) Timestamp when the job finished, if completed
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resources_allocated:
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type: object
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additionalProperties:
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@ -8107,10 +8117,15 @@ components:
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- type: string
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- type: array
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- type: object
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description: >-
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(Optional) Information about computational resources allocated to the
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job
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checkpoints:
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type: array
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items:
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$ref: '#/components/schemas/Checkpoint'
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description: >-
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List of model checkpoints created during training
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additionalProperties: false
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required:
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- job_uuid
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@ -10491,12 +10506,18 @@ components:
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properties:
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reward_scale:
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type: number
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description: Scaling factor for the reward signal
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reward_clip:
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type: number
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description: >-
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Maximum absolute value for reward clipping
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epsilon:
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type: number
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description: >-
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Small value added for numerical stability
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gamma:
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type: number
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description: Discount factor for future rewards
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additionalProperties: false
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required:
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- reward_scale
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@ -10504,25 +10525,41 @@ components:
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- epsilon
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- gamma
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title: DPOAlignmentConfig
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description: >-
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Configuration for Direct Preference Optimization (DPO) alignment.
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DataConfig:
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type: object
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properties:
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dataset_id:
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type: string
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description: >-
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Unique identifier for the training dataset
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batch_size:
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type: integer
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description: Number of samples per training batch
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shuffle:
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type: boolean
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description: >-
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Whether to shuffle the dataset during training
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data_format:
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$ref: '#/components/schemas/DatasetFormat'
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description: >-
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Format of the dataset (instruct or dialog)
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validation_dataset_id:
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type: string
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description: >-
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(Optional) Unique identifier for the validation dataset
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packed:
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type: boolean
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default: false
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description: >-
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(Optional) Whether to pack multiple samples into a single sequence for
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efficiency
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train_on_input:
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type: boolean
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default: false
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description: >-
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(Optional) Whether to compute loss on input tokens as well as output tokens
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additionalProperties: false
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required:
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- dataset_id
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@ -10530,40 +10567,59 @@ components:
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- shuffle
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- data_format
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title: DataConfig
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description: >-
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Configuration for training data and data loading.
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DatasetFormat:
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type: string
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enum:
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- instruct
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- dialog
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title: DatasetFormat
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description: Format of the training dataset.
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EfficiencyConfig:
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type: object
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properties:
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enable_activation_checkpointing:
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type: boolean
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default: false
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description: >-
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(Optional) Whether to use activation checkpointing to reduce memory usage
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enable_activation_offloading:
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type: boolean
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default: false
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description: >-
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(Optional) Whether to offload activations to CPU to save GPU memory
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memory_efficient_fsdp_wrap:
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type: boolean
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default: false
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description: >-
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(Optional) Whether to use memory-efficient FSDP wrapping
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fsdp_cpu_offload:
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type: boolean
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default: false
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description: >-
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(Optional) Whether to offload FSDP parameters to CPU
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additionalProperties: false
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title: EfficiencyConfig
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description: >-
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Configuration for memory and compute efficiency optimizations.
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OptimizerConfig:
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type: object
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properties:
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optimizer_type:
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$ref: '#/components/schemas/OptimizerType'
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description: >-
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Type of optimizer to use (adam, adamw, or sgd)
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lr:
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type: number
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description: Learning rate for the optimizer
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weight_decay:
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type: number
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description: >-
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Weight decay coefficient for regularization
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num_warmup_steps:
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type: integer
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description: Number of steps for learning rate warmup
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additionalProperties: false
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required:
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- optimizer_type
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@ -10571,6 +10627,8 @@ components:
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- weight_decay
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- num_warmup_steps
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title: OptimizerConfig
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description: >-
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Configuration parameters for the optimization algorithm.
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OptimizerType:
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type: string
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enum:
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@ -10578,35 +10636,53 @@ components:
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- adamw
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- sgd
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title: OptimizerType
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description: >-
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Available optimizer algorithms for training.
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TrainingConfig:
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type: object
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properties:
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n_epochs:
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type: integer
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description: Number of training epochs to run
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max_steps_per_epoch:
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type: integer
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default: 1
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description: Maximum number of steps to run per epoch
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gradient_accumulation_steps:
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type: integer
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default: 1
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description: >-
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Number of steps to accumulate gradients before updating
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max_validation_steps:
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type: integer
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default: 1
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description: >-
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(Optional) Maximum number of validation steps per epoch
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data_config:
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$ref: '#/components/schemas/DataConfig'
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description: >-
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(Optional) Configuration for data loading and formatting
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optimizer_config:
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$ref: '#/components/schemas/OptimizerConfig'
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description: >-
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(Optional) Configuration for the optimization algorithm
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efficiency_config:
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$ref: '#/components/schemas/EfficiencyConfig'
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description: >-
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(Optional) Configuration for memory and compute optimizations
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dtype:
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type: string
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default: bf16
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description: >-
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(Optional) Data type for model parameters (bf16, fp16, fp32)
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additionalProperties: false
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required:
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- n_epochs
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- max_steps_per_epoch
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- gradient_accumulation_steps
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title: TrainingConfig
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description: >-
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Comprehensive configuration for the training process.
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PreferenceOptimizeRequest:
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type: object
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properties:
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@ -11535,24 +11611,38 @@ components:
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type: string
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const: LoRA
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default: LoRA
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description: Algorithm type identifier, always "LoRA"
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lora_attn_modules:
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type: array
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items:
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type: string
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description: >-
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List of attention module names to apply LoRA to
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apply_lora_to_mlp:
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type: boolean
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description: Whether to apply LoRA to MLP layers
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apply_lora_to_output:
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type: boolean
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description: >-
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Whether to apply LoRA to output projection layers
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rank:
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type: integer
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description: >-
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Rank of the LoRA adaptation (lower rank = fewer parameters)
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alpha:
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type: integer
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description: >-
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LoRA scaling parameter that controls adaptation strength
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use_dora:
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type: boolean
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default: false
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description: >-
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(Optional) Whether to use DoRA (Weight-Decomposed Low-Rank Adaptation)
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quantize_base:
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type: boolean
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default: false
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description: >-
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(Optional) Whether to quantize the base model weights
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additionalProperties: false
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required:
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- type
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@ -11562,6 +11652,8 @@ components:
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- rank
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- alpha
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title: LoraFinetuningConfig
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description: >-
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Configuration for Low-Rank Adaptation (LoRA) fine-tuning.
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QATFinetuningConfig:
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type: object
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properties:
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@ -11569,16 +11661,22 @@ components:
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type: string
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const: QAT
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default: QAT
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description: Algorithm type identifier, always "QAT"
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quantizer_name:
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type: string
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description: >-
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Name of the quantization algorithm to use
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group_size:
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type: integer
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description: Size of groups for grouped quantization
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additionalProperties: false
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required:
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- type
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- quantizer_name
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- group_size
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title: QATFinetuningConfig
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description: >-
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Configuration for Quantization-Aware Training (QAT) fine-tuning.
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SupervisedFineTuneRequest:
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type: object
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properties:
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