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* gemma4 support * fixes * chore: lint
105 lines
2.7 KiB
YAML
105 lines
2.7 KiB
YAML
# Gemma 4 26B-A4B MoE QLoRA with ScatterMoE kernels
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#
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# Validated: 50 steps on FineTome-100k, loss 7.4 -> 2.4, single RTX 5090 (32GB)
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#
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# Key notes:
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# - Flash Attention 2 is NOT supported (global_head_dim=512 > FA2 max of 256).
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# Use sdp_attention instead.
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# - Gemma 4 is multimodal (text+vision+audio). For text-only SFT, restrict
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# LoRA to the text backbone via lora_target_linear_modules regex.
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# - MoE experts use `experts_implementation: scattermoe` — Gemma 4 embeds MoE
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# directly in the decoder layer (no SparseMoeBlock), so we register ScatterMoE
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# via the transformers ExpertsInterface.
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# - Expert LoRA targets are `experts.gate_up_proj` / `experts.down_proj`
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# (no `mlp.` prefix, unlike Qwen/Mixtral).
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# - micro_batch_size: 1 fits 2048 seq_len on 32GB GPU with SDP attention.
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# Use micro_batch_size: 4 with 1024 seq_len, or on 48GB+ GPUs.
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base_model: google/gemma-4-26B-A4B
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plugins:
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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- axolotl.integrations.kernels.KernelsPlugin
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- axolotl.integrations.liger.LigerPlugin
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use_kernels: true
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use_scattermoe: true
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experts_implementation: scattermoe
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torch_compile: false
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liger_layer_norm: true
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liger_rope: true
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liger_rms_norm: true
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liger_glu_activation: true
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liger_rms_norm_gated: true
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strict: false
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chat_template: gemma4
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datasets:
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- path: mlabonne/FineTome-100k
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type: chat_template
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split: train[:10%]
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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: 0.05
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output_dir: ./outputs/gemma4-26b-a4b-qlora
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sequence_len: 2048
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sample_packing: true
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load_in_4bit: true
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quantize_moe_experts: true
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adapter: qlora
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lora_r: 16
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lora_alpha: 32
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lora_dropout: 0
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# Restrict LoRA to text backbone only (skip vision/audio encoders).
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# lora_target_modules is intentionally empty — all module targeting is done
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# via regex in lora_target_linear_modules below.
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lora_target_modules: []
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lora_target_linear_modules:
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- language_model\.model\.layers\.\d+\.self_attn\.(q|k|v|o)_proj
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# MoE expert LoRA (3D Parameter tensors, not nn.Linear)
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lora_target_parameters:
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- experts.gate_up_proj
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- experts.down_proj
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lora_mlp_kernel: false
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lora_qkv_kernel: false
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lora_o_kernel: false
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bnb_config_kwargs:
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bnb_4bit_use_double_quant: true
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wandb_project: gemma4-qlora
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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_torch_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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activation_offloading: true
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logging_steps: 1
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# FA2 not supported — Gemma4 global_head_dim=512 exceeds FA2 max of 256
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flash_attention: false
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sdp_attention: true
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warmup_ratio: 0.1
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evals_per_epoch: 4
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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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