68 lines
1.4 KiB
YAML
68 lines
1.4 KiB
YAML
base_model: alpindale/Llama-3.2-11B-Vision-Instruct
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# optionally might have model_type or tokenizer_type or processor_type
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processor_type: AutoProcessor
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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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strict: false
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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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chat_template: llama3_2_vision
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datasets:
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- path: HuggingFaceH4/llava-instruct-mix-vsft
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type: chat_template
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split: train[:1%]
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field_messages: messages
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.0
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output_dir: ./outputs/out
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adapter: lora
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lora_model_dir:
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sequence_len: 8192
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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|cross_attn|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: 1
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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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train_on_inputs: false
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group_by_length: false
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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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local_rank:
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logging_steps: 1
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flash_attention: true
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eager_attention:
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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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debug:
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deepspeed:
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weight_decay: 0.0
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fsdp:
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fsdp_config:
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