79 lines
1.7 KiB
YAML
79 lines
1.7 KiB
YAML
base_model: mistralai/Voxtral-Mini-3B-2507
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processor_type: VoxtralProcessor
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# Automatically upload checkpoint and final model to HF
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# hub_model_id: username/custom_model_name
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# Enable to use mistral-common tokenizer
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tokenizer_use_mistral_common: true
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plugins:
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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# for use with fft to only train on language model layers
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# unfrozen_parameters:
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# - language_model.model.*
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# - lm_head
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# - embed_tokens
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load_in_4bit: true
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# these 3 lines are needed for now to handle vision chat templates w images
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skip_prepare_dataset: true
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remove_unused_columns: false
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sample_packing: false
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# gemma3 doesn't seem to play nice with ddp
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ddp_find_unused_parameters: true
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eot_tokens:
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- <end_of_turn>
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# sample dataset below requires downloading audio/image in advance
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# wget https://huggingface.co/datasets/Nanobit/text-audio-2k-test/resolve/main/En-us-African_elephant.oga
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datasets:
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- path: NanoBit/text-audio-2k-test
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type: chat_template
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dataset_prepared_path:
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val_set_size: 0.01
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output_dir: ./outputs/out
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adapter: qlora
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lora_model_dir:
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sequence_len: 2048
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pad_to_sequence_len: false
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lora_r: 32
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_modules: 'language_model.model.layers.[\d]+.(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
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wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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gradient_accumulation_steps: 4
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micro_batch_size: 2
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num_epochs: 1
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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bf16: true
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fp16:
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tf32: true
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gradient_checkpointing: true
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gradient_checkpointing_kwargs:
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use_reentrant: false
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logging_steps: 1
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flash_attention: true
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warmup_ratio: 0.1
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evals_per_epoch: 1
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saves_per_epoch: 1
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weight_decay: 0.0
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