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Fine Tuning API Specifications

v1alpha1​

#syntax=ghcr.io/sozercan/aikit:latest
apiVersion: # required. only v1alpha1 is supported at the moment
baseModel: # required. any base model from Huggingface. for unsloth, see for 4bit pre-quantized models: https://huggingface.co/unsloth
datasets:
  - source: # required. this can be a Huggingface dataset repo or a URL pointing to a JSON file
    type: # required. can be "alpaca". only alpaca is supported at the moment
config:
  unsloth:
    packing: # optional. defaults to false. can make training 5x faster for short sequences.
    maxSeqLength: # optional. defaults to 2048
    loadIn4bit: # optional. defaults to true
    batchSize: # optional. default to 2
    gradientAccumulationSteps: # optional. defaults to 4
    warmupSteps: # optional. defaults to 10
    maxSteps: # optional. defaults to 60
    learningRate: # optional. defaults to 0.0002
    loggingSteps: # optional. defaults to 1
    optimizer: # optional. defaults to adamw_8bit
    weightDecay: # optional. defaults to 0.01
    lrSchedulerType: # optional. defaults to linear
    seed: # optional. defaults to 42
output:
  quantize: # optional. defaults to q4_k_m. for unsloth, see for allowed quantization methods: https://github.com/unslothai/unsloth/wiki#saving-to-gguf.
  name: # optional. defaults to "aikit-model"

Example:

#syntax=ghcr.io/sozercan/aikit:latest
apiVersion: v1alpha1
baseModel: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
datasets:
  - source: yahma/alpaca-cleaned
    type: alpaca
config:
  unsloth:
    packing: false
    maxSeqLength: 2048
    loadIn4bit: true
    batchSize: 2
    gradientAccumulationSteps: 4
    warmupSteps: 10
    maxSteps: 60
    learningRate: 0.0002
    loggingSteps: 1
    optimizer: adamw_8bit
    weightDecay: 0.01
    lrSchedulerType: linear
    seed: 42
output:
  quantize: q4_k_m
  name: model