22810c97b7
* use warmup_ratio as a better default than warmup steps since it's data dependent * replace remainder of warmup_steps
82 lines
1.6 KiB
YAML
82 lines
1.6 KiB
YAML
base_model: meta-llama/Llama-3.2-3B
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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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load_in_8bit: false
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load_in_4bit: false
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strict: false
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plugins:
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- axolotl.integrations.liger.LigerPlugin
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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_layer_norm: true
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liger_fused_linear_cross_entropy: true
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datasets:
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- path: yahma/alpaca-cleaned
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type: alpaca
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output_dir: ./outputs/qat_out/
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sample_packing: true
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sequence_len: 512
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flex_attention: true
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flex_attn_compile_kwargs:
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dynamic: false
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mode: max-autotune-no-cudagraphs
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qat:
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activation_dtype: int8
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weight_dtype: int4
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group_size: 32
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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: 16
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num_epochs: 1
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optimizer: adamw_torch_fused
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cosine_constant_lr_ratio: 0
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cosine_min_lr_ratio: 1.0
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learning_rate: 2e-5
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save_only_model: true
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bf16: true
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resume_from_checkpoint:
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logging_steps: 1
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evals_per_epoch: 1
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saves_per_epoch: 1
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warmup_ratio: 0.1
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weight_decay: 0.0
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fsdp:
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- full_shard
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- auto_wrap
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fsdp_config:
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fsdp_version: 2
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fsdp_offload_params: false
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fsdp_cpu_ram_efficient_loading: true
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fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
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fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
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fsdp_state_dict_type: FULL_STATE_DICT
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fsdp_sharding_strategy: FULL_SHARD
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fsdp_reshard_after_forward: true
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fsdp_activation_checkpointing: true
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special_tokens:
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pad_token: <|end_of_text|>
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# save_first_step: true # uncomment this to validate checkpoint saving works with your config
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