133 lines
3.4 KiB
Plaintext
133 lines
3.4 KiB
Plaintext
---
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library_name: transformers
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tags:
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- generated_from_trainer
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datasets:
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- gs://fine-tuning-634768b5-ef69-4ea6-9fd2-c9379a5ee381/copymediadatatask/execution_artifacts/clean_train.jsonl
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model-index:
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- name: tmp/output_dir/gcs/fine-tuning-634768b5-ef69-4ea6-9fd2-c9379a5ee381/postprocess/node-0/checkpoints/final
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.13.0.dev0`
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```yaml
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base_model: gs://vertex-model-garden-restricted-us/gemma3/gemma-3-12b-it
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# gemma3 doesn't seem to play nice with ddp
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ddp_find_unused_parameters: true
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experimental_skip_move_to_device: true # prevent OOM by NOT putting model to GPU before sharding
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plugins:
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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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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chat_template: gemma3
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eot_tokens:
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- <end_of_turn>
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dataset_prepared_path: last_run_prepared
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output_dir: /workspace/outputs/out
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sequence_len: 8192
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sample_packing: true
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eval_sample_packing: false
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use_kernels: true
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micro_batch_size: 2
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gradient_accumulation_steps: 1
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num_epochs: 3
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optimizer: adamw_torch_fused
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learning_rate: 1e-5
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lr_scheduler: cosine
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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: true
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activation_offloading: true
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val_set_size: 0
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eval_strategy: "epoch"
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save_strategy: 'no'
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include_tokens_per_second: true
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save_safetensors: true
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use_tensorboard: true
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fsdp_version: 1
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fsdp_config:
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fsdp_limit_all_gathers: true
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fsdp_sync_module_states: true
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fsdp_offload_params: true
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fsdp_use_orig_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: Gemma3DecoderLayer
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fsdp_state_dict_type: SHARDED_STATE_DICT
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fsdp_sharding_strategy: SHARD_GRAD_OP
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fsdp_backward_prefetch: BACKWARD_PRE
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final_state_dict_type: FULL_STATE_DICT
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```
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</details><br>
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# tmp/output_dir/gcs/fine-tuning-634768b5-ef69-4ea6-9fd2-c9379a5ee381/postprocess/node-0/checkpoints/final
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This model was trained from scratch on the gs://fine-tuning-634768b5-ef69-4ea6-9fd2-c9379a5ee381/copymediadatatask/execution_artifacts/clean_train.jsonl dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-07
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- total_train_batch_size: 16
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- total_eval_batch_size: 16
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 20
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- training_steps: 675
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### Training results
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### Framework versions
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- Transformers 4.55.4
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- Pytorch 2.7.1+cu126
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- Datasets 4.0.0
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- Tokenizers 0.21.4
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