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fine tuning apis
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Hardik Shah
parent
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commit
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2 changed files with 400 additions and 0 deletions
266
fine_tuning.yaml
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266
fine_tuning.yaml
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openapi: 3.0.0
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info:
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title: Fine Tuning API
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version: 1.0.0
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description: API for managing fine tuning jobs for machine learning models.
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paths:
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/fine_tuning/jobs/submit:
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post:
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summary: Submit a fine tuning job
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description: Submit a fine tuning job with the specified configuration.
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requestBody:
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required: true
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content:
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application/json:
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schema:
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$ref: '#/components/schemas/Config'
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responses:
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200:
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description: Successfully submitted the fine tuning job.
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content:
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application/json:
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schema:
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$ref: '#/components/schemas/FineTuningJob'
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/fine_tuning/jobs/status:
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get:
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summary: Gets last N fine tuning jobs
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description: Retrieve the status of the last N fine tuning jobs based on the provided job ID.
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parameters:
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- in: query
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name: job_id
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schema:
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type: string
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required: true
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description: The ID of the job to retrieve status for.
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responses:
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200:
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description: Successfully retrieved the job status.
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content:
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application/json:
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schema:
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$ref: '#/components/schemas/FineTuningJob'
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/fine_tuning/jobs/cancel:
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post:
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summary: Cancel provided job
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description: Cancel the fine tuning job with the specified job ID.
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requestBody:
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required: true
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content:
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application/json:
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schema:
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type: object
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properties:
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job_id:
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type: string
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responses:
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200:
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description: Successfully cancelled the fine tuning job.
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content:
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application/json:
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schema:
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$ref: '#/components/schemas/FineTuningJob'
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/fine_tuning/jobs/tail:
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get:
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summary: Tail logs of a particular job
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description: Stream the logs of a particular job in real-time. This endpoint supports streaming responses.
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parameters:
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- in: query
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name: job_id
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schema:
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type: string
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required: true
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description: The ID of the job to tail logs for.
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responses:
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200:
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description: Streaming logs in real-time.
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content:
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application/x-ndjson:
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schema:
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type: object
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properties:
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logs:
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type: array
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items:
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$ref: '#/components/schemas/Log'
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headers:
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Content-Type:
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schema:
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type: string
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default: 'application/x-ndjson'
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Transfer-Encoding:
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schema:
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type: string
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default: 'chunked'
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components:
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schemas:
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Message:
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# keep in sync with /chat_completion
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TrainingDataItem:
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type: object
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properties:
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dialog:
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type: array
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items:
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$ref: '#/components/schemas/Message'
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keep_loss:
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type: array
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items:
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type: boolean
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WandBLogger:
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type: object
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properties:
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project:
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type: string
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description: The project name in WandB where logs will be stored.
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DiskLogger:
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type: object
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properties:
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filename:
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type: string
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description: The filename where logs will be stored on disk.
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FullFineTuneOptions:
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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: true
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memory_efficient_fsdp_wrap:
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type: boolean
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default: true
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fsdp_cpu_offload:
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type: boolean
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default: true
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LoraFineTuneOptions:
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type: object
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properties:
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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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apply_lora_to_mlp:
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type: boolean
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default: false
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apply_lora_to_output:
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type: boolean
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default: false
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lora_rank:
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type: integer
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lora_alpha:
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type: integer
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FineTuningOptions:
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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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batch_size:
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type: integer
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lr:
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type: number
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format: float
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gradient_accumulation_steps:
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type: integer
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seed:
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type: integer
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shuffle:
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type: boolean
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custom_training_options:
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oneOf:
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- $ref: '#/components/schemas/FullFineTuneOptions'
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- $ref: '#/components/schemas/LoraFineTuneOptions'
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discriminator:
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propertyName: finetuning_type
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extras:
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# json to put other config overrides that are required by torchtune
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type: object
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additionalProperties: true
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Config:
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type: object
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properties:
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model:
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type: string
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description: The model identifier that you want to fine tune.
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data:
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type: string
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format: uri
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description: Path to the JSONL file with each row representing a TrainingDataItem.
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validation_data:
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type: string
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format: uri
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description: Path to the JSONL file used for validation metrics.
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fine_tuning_options:
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$ref: '#/components/schemas/FineTuningOptions'
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logger:
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oneOf:
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- $ref: '#/components/schemas/DiskLogger'
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- $ref: '#/components/schemas/WandBLogger'
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discriminator:
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propertyName: log_type
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overrides:
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# eg. --nproc_per_node 4 instead of default that we need to pass through to torchrun
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# when running locally
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type: string
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description: Custom override options for the fine tuning process.
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metadata:
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type: object
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additionalProperties: true
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FineTuningJob:
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type: object
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properties:
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job_id:
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type: string
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description: Unique identifier for the fine tuning job.
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created:
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type: string
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format: date-time
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description: The creation date and time of the job.
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finished_at:
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type: string
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format: date-time
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description: The completion date and time of the job.
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status:
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type: string
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enum: [validation, queued, running, failed, success, cancelled]
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description: The current status of the job.
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error_path:
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type: string
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format: uri
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description: Path to the error log file.
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checkpoints:
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type: array
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items:
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type: string
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format: uri
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description: List of paths to checkpoint files for various epochs.
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logs:
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type: string
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format: uri
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description: Path to the logs, either local or a WandB URI.
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input_config:
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$ref: '#/components/schemas/Config'
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metadata:
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type: object
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additionalProperties: true
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Log:
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type: object
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properties:
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message:
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type: string
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description: The log message.
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timestamp:
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type: string
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format: date-time
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description: The timestamp of the log message.
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134
simple_view/fine_tuning.yml
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134
simple_view/fine_tuning.yml
Normal file
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@ -0,0 +1,134 @@
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# Fine Tuning APIs
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== Schema ==
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TrainingDataItem:
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dialog: List[Message]
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keep_loss: List[bool]
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WandBLogger:
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project: str
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DiskLogger:
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# log_dir will be pre-configured in environment
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filename: str
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FullFineTuneOptions:
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enable_activation_checkpointing: True
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memory_efficient_fsdp_wrap: True
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fsdp_cpu_offload: True
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LoraFineTuneOptions:
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lora_attn_modules: ['q_proj', 'v_proj']
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apply_lora_to_mlp: False
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apply_lora_to_output: False
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lora_rank: 8
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lora_alpha: 16
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FineTuningOptions:
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n_epochs: int
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batch_size: int
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lr: float
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gradient_accumulation_steps: int
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seed: int
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shuffle: bool
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# Unions in OpenAPI with a reference field that can help disambiguate
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custom_training_options:
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discriminator:
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propertyName: fine_tuning_type
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mapping:
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fft: FullFineTuneOptions
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lora: LoraFineTuneOptions
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# other options that can be passed in
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extras: json
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Config:
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model: str # model that you want to fine tune
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data: Path # jsonl with each row representing a TrainingDataItem
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validation_data: Path # same as data but to get validation metrics on
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# fine tuning args
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fine_tuning_options: FineTuningOptions
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# metric logging
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logger:
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discriminator:
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propertyName: log_type
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mapping:
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disk: DiskLogger
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wandb: WandBLogger
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# Override options
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# eg. --nproc_per_node 4 insted of defaults,
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# this might be impl specific and can allow for various customizations
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overrides: str
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metadata: json # to carry over to job details
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FineTuningJob:
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job_id: str
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created: str # format date-time
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finished_at: str # format date-time
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status: str # enum - validation, queued, running, failed, success, cancelled
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error_path: Path # error logging
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checkpoints: List[Path] # checkpoints for various epochs
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logs: Path # local path / wandb uri
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input_config: Config # config used to submit this job
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metadata: json # carried over rom user provided input
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Log:
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message: string # The log message.
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timestamp: string # format: date-time
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== Callsites ==
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callsite:
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/fine_tuning/jobs/submit
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request_type:
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post
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description:
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Submit a fine tuning job
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request:
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config: Config
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response:
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fine_tuning_job: FineTuningJob
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callsite:
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/fine_tuning/jobs/status
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request_type:
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get
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description:
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Gets last N fine tuning jobs
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request:
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job_id: str
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response:
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fine_tuning_job: FineTuningJob
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callsite:
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/fine_tuning/jobs/cancel
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request_type:
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post
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description:
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Cancel provided job
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request:
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job_id: str
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response:
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fine_tuning_job: FineTuningJob
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callsite:
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/fine_tuning/jobs/tail
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request_type:
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get
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description:
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Tail logs of a particular job
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request:
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job_id: str
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response:
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logs: List[Log]
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streaming:
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enabled: True
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chunkSize: 1024
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Reference in a new issue