113d275bd9
* qwen docs + new config * docss lint * simplify comments * read me * lint comments * Update docs/multimodal.qmd * Update docs/multimodal.qmd * Update examples/qwen3.5/9b-fft-vision.yaml * chore: fix link and incorrect points --------- Co-authored-by: NanoCode012 <kevinvong@rocketmail.com> Co-authored-by: NanoCode012 <nano@axolotl.ai>
69 lines
1.4 KiB
YAML
69 lines
1.4 KiB
YAML
base_model: Qwen/Qwen3.5-9B
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processor_type: AutoProcessor
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# These 3 lines are required for vision/multimodal training
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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: qwen3_5
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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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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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# Targets the language model attention and MLP layers.
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# Qwen3.5 is early-fusion: all layers (including those seeing vision tokens) share
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# the same transformer stack, so standard attention targets work for both modalities.
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lora_target_modules:
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- q_proj
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- k_proj
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- v_proj
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- o_proj
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- down_proj
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- up_proj
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# Uncomment to also target the linear attention (GatedDeltaNet) projections:
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# - linear_attn.in_proj_qkv
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# - linear_attn.in_proj_z
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# - linear_attn.out_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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bf16: true
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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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