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https://github.com/ml-explore/mlx-examples.git
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e150621095
* initial commmit * adding more customized YAML configuartion * update YAML example file * Changed the switch to set opt_class * removing muon * using default arguments * udpate
90 lines
2.0 KiB
YAML
90 lines
2.0 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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# The fine-tuning method: "lora", "dora", or "full".
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fine_tune_type: lora
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# The Optimizer with its possible inputs
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optimizer: adamw
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# optimizer_config:
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# adamw:
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# betas: [0.9, 0.98]
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# eps: 1e-6
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# weight_decay: 0.05
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# bias_correction: 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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num_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: 1000
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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_path: "adapters"
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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: 100
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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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scale: 20.0
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dropout: 0.0
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# Schedule can only be specified in a config file, uncomment to use.
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#lr_schedule:
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# name: cosine_decay
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# warmup: 100 # 0 for no warmup
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# warmup_init: 1e-7 # 0 if not specified
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# arguments: [1e-5, 1000, 1e-7] # passed to scheduler
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#hf_dataset:
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# name: "billsum"
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# train_split: "train[:1000]"
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# valid_split: "train[-100:]"
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# prompt_feature: "text"
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# completion_feature: "summary"
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