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d2a99172a6
* Add dropout parameter to lora configuration A dropout parameter has been added to the lora configuration settings in lora_config.yaml. The LoRALinear class in utils.py has been updated to take this new parameter. Additionally, a AttributeError: 'types.SimpleNamespace' object has no attribute 'prompt' related to `args.prompt` has been removed from lora.py. * Update lora_config.yaml Set dropout to 0.0 in the sample config file * format --------- Co-authored-by: Awni Hannun <awni@apple.com>
63 lines
1.3 KiB
YAML
63 lines
1.3 KiB
YAML
# The path to the local model directory or Hugging Face repo.
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model: "mlx_model"
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# Whether or not to train (boolean)
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train: true
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# Directory with {train, valid, test}.jsonl files
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data: "/path/to/training/data"
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# The PRNG seed
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seed: 0
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# Number of layers to fine-tune
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lora_layers: 16
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# Minibatch size.
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batch_size: 4
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# Iterations to train for.
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iters: 100
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# Number of validation batches, -1 uses the entire validation set.
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val_batches: 25
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# Adam learning rate.
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learning_rate: 1e-5
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# Number of training steps between loss reporting.
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steps_per_report: 10
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# Number of training steps between validations.
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steps_per_eval: 200
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# Load path to resume training with the given adapter weights.
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resume_adapter_file: null
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# Save/load path for the trained adapter weights.
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adapter_file: "adapters.npz"
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# Save the model every N iterations.
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save_every: 100
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# Evaluate on the test set after training
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test: false
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# Number of test set batches, -1 uses the entire test set.
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test_batches: 500
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# Maximum sequence length.
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max_seq_length: 2048
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# Use gradient checkpointing to reduce memory use.
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grad_checkpoint: false
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# LoRA parameters can only be specified in a config file
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lora_parameters:
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# The layer keys to apply LoRA to.
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# These will be applied for the last lora_layers
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keys: ["self_attn.q_proj", "self_attn.v_proj"]
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rank: 8
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alpha: 16.0
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scale: 10.0
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dropout: 0.0
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