418933f0d1
* feat: add internvl3_5 * fix: add timm instructions * chore: add kimi-linear to cce doc * feat: update internvl example * chore: pin revision * chore: remove from multipack * fix: add to multimodal array * fix: internvl use hf version * feat: update cce * chore: lint * fix: list for image_size * chore: add docs vram usage * feat: enable cce * fix: no need trust remote code * fix: inconsistent timm version
62 lines
1.2 KiB
YAML
62 lines
1.2 KiB
YAML
base_model: OpenGVLab/InternVL3_5-8B-HF
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processor_type: AutoProcessor
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plugins:
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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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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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: 0.01
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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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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: 'model.language_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: 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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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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weight_decay: 0.0
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# save_first_step: true # uncomment this to validate checkpoint saving works with your config
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