87 lines
1.7 KiB
YAML
87 lines
1.7 KiB
YAML
base_model: axolotl-quants/Llama-4-Scout-17B-16E-Linearized-bnb-nf4-bf16
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model_type: Llama4ForConditionalGeneration
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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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plugins:
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- axolotl.integrations.liger.LigerPlugin
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liger_glu_activation: true
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liger_rms_norm: true
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liger_layer_norm: true
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llama4_linearized_experts: true
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load_in_4bit: true
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adapter: qlora
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lora_r: 32
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lora_alpha: 64
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lora_target_modules:
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- self_attn.q_proj
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- self_attn.k_proj
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- self_attn.v_proj
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- self_attn.o_proj
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- shared_expert.gate_proj
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- shared_expert.up_proj
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- shared_expert.down_proj
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# - experts.gate_projs.[0-9]+$
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# - experts.up_projs.[0-9]+$
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# - experts.down_projs.[0-9]+$
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lora_modules_to_save:
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# - lm_head
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# - embed_tokens
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lora_mlp_kernel: true
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lora_qkv_kernel: true
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lora_o_kernel: true
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chat_template: llama4
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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[:20%]
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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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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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sequence_len: 4096 # up to 8k will work on a single H100
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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: 1
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optimizer: adamw_torch_4bit
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lr_scheduler: cosine
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learning_rate: 1e-4
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bf16: true
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tf32: true
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logging_steps: 1
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flash_attention: true
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gradient_checkpointing: offload
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gradient_checkpointing_kwargs:
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use_reentrant: false
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warmup_steps: 20
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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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special_tokens:
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pad_token: <|finetune_right_pad_id|>
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eos_token: <|eot|>
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