f2155eaf79
* feat: add trackio as experiment tracking integration - Add TrackioConfig to integrations schema with project_name, run_name, and space_id - Create trackio_.py module for environment setup - Add is_trackio_available() utility function - Integrate trackio with report_to in trainer builder - Add trackio callback for experiment tracking - Add trackio config keys to gpt-oss example YAMLs - Trackio runs locally by default, syncs to HF Space if space_id provided * changes * changes * changes * changes * changes * changes * changes * Update requirements.txt * don't allow pydantic 2.12 for now --------- Co-authored-by: Abubakar Abid <aaabid93@gmail.com> Co-authored-by: Wing Lian <wing@axolotl.ai>
73 lines
1.8 KiB
YAML
73 lines
1.8 KiB
YAML
# the original mxfp4 quantized model is not supported with FSDP cpu_ram_efficient_loading
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# FSDP cpu_ram_efficient_loading is used to reduce the initial CPU memory usage when loading the model
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base_model: axolotl-ai-co/gpt-oss-120b-dequantized
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use_kernels: false
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dp_shard_size: 16 # requires 2x8xH100 nodes
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plugins:
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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experimental_skip_move_to_device: true # prevent OOM by NOT putting model to GPU before sharding
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datasets:
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- path: HuggingFaceH4/Multilingual-Thinking
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type: chat_template
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field_thinking: thinking
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template_thinking_key: thinking
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dataset_prepared_path: last_run_prepared
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val_set_size: 0
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output_dir: ./outputs/gpt-oss-out/
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save_total_limit: 2 # the 120B model can use up to 720GB of disk space per checkpoint, so let's only keep the last 2
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sequence_len: 4096
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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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trackio_project_name:
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trackio_run_name:
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trackio_space_id:
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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_fused # 8bit optimizers do not work with FSDP2 offload
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lr_scheduler: constant_with_warmup
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learning_rate: 2e-5
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bf16: true
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tf32: true
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flash_attention: true
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attn_implementation: kernels-community/vllm-flash-attn3 # this is not needed if using flash_attn >= 2.8.3
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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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saves_per_epoch: 1
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warmup_ratio: 0.03
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special_tokens:
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eot_tokens:
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- "<|end|>"
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fsdp_version: 2
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fsdp_config:
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offload_params: true
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state_dict_type: SHARDED_STATE_DICT
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auto_wrap_policy: TRANSFORMER_BASED_WRAP
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transformer_layer_cls_to_wrap: GptOssDecoderLayer
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reshard_after_forward: true
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cpu_ram_efficient_loading: true
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