75 lines
1.5 KiB
YAML
75 lines
1.5 KiB
YAML
base_model: google/gemma-3n-E2B-it
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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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plugins:
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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cut_cross_entropy: true
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load_in_8bit: false
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load_in_4bit: true
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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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# - model.language_model.*
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# - lm_head
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# - embed_tokens
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chat_template: gemma3n
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eot_tokens:
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- <end_of_turn>
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datasets:
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- path: cgato/SlimOrcaDedupCleaned
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type: chat_template
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split: train[:1%]
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field_messages: conversations
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message_property_mappings:
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role: from
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content: value
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val_set_size: 0.0
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output_dir: ./outputs/out
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adapter: qlora
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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_linear: # Does not work with gemma3n currently
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lora_target_modules: 'model.language_model.layers.[\d]+.(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
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sequence_len: 2048
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sample_packing: true
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eval_sample_packing: true
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pad_to_sequence_len: true
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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: 1
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micro_batch_size: 1
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num_epochs: 4
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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: auto
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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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resume_from_checkpoint:
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logging_steps: 1
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# flash_attention: true # Any attention impl does not work with gemma3n now
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warmup_ratio: 0.1
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evals_per_epoch:
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saves_per_epoch: 1
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weight_decay: 0.0
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special_tokens:
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