c57acef2c7
* lora target modules with regex * updates * fsdp for non moe * update wording * chore: cleanup and lint * chore: cleanup docs from merge --------- Co-authored-by: NanoCode012 <nano@axolotl.ai>
71 lines
1.4 KiB
YAML
71 lines
1.4 KiB
YAML
base_model: Qwen/Qwen3.5-27B
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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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plugins:
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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strict: false
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chat_template: qwen3_5
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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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val_set_size: 0.0
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output_dir: ./outputs/out
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dataset_prepared_path: last_run_prepared
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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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adapter: qlora
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lora_r: 16
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lora_alpha: 32
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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 below to also target the linear attention projections.
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# These use separate in_proj_qkv / in_proj_z / out_proj (Qwen3.5-specific).
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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: 2
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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: 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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gradient_checkpointing_kwargs:
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use_reentrant: false
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resume_from_checkpoint:
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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: 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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