ee6e00f88e
Model: CaffeineThief/ttp_sft_kanana-1.5_steps_tram-step1-seed44 Source: Original Platform
137 lines
3.1 KiB
Markdown
137 lines
3.1 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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base_model: kakaocorp/kanana-1.5-2.1b-instruct-2505
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tags:
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- axolotl
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- generated_from_trainer
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datasets:
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- tram2_train_step1.jsonl
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model-index:
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- name: ttp_sft_kanana-1.5_steps_tram-step1-seed44
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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: `0.12.2`
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```yaml
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base_model: kakaocorp/kanana-1.5-2.1b-instruct-2505
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hf_cache_dir: ../../../../data5/models
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load_in_8bit: false
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load_in_4bit: false
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datasets:
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- path: tram2_train_step1.jsonl
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type: chat_template
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split: train
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seed: 44
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dataset_prepared_path: preprocess
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val_set_size: 0
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output_dir: ./outputs-kanana-steps-tram-step1-seed44
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dataloader_num_workers: 32
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seed: 44
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sequence_len: 3072
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sample_packing: false
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eval_sample_packing: false
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pad_to_sequence_len: 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_swiglu: true
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liger_fused_linear_cross_entropy: true
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wandb_project: TTP_SFT_LLM_RE
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wandb_entity:
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wandb_watch:
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wandb_name: ttp_sft_kanana-1.5_steps_tram-step1-seed44
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wandb_log_model:
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hub_model_id: CaffeineThief/ttp_sft_kanana-1.5_steps_tram-step1-seed44
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hub_private_repo: false
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gradient_accumulation_steps: 1
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# micro_batch_size: 16 # GPU 3장
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micro_batch_size: 24 # GPU 2장
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num_epochs: 3
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optimizer: adamw_torch_fused
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lr_scheduler: cosine
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learning_rate: 2e-5
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bf16: auto
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tf32: false
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gradient_checkpointing: 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: 0.05
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weight_decay: 0.01
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evals_per_epoch: 1
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saves_per_epoch: 1
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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_state_dict_type: FULL_STATE_DICT
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fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
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fsdp_activation_checkpointing: true
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```
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</details><br>
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# ttp_sft_kanana-1.5_steps_tram-step1-seed44
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This model is a fine-tuned version of [kakaocorp/kanana-1.5-2.1b-instruct-2505](https://huggingface.co/kakaocorp/kanana-1.5-2.1b-instruct-2505) on the tram2_train_step1.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: 2e-05
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- train_batch_size: 24
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- eval_batch_size: 24
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- seed: 44
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- distributed_type: multi-GPU
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- num_devices: 2
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- total_train_batch_size: 48
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- total_eval_batch_size: 48
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- optimizer: Use 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: 10
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- training_steps: 204
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### Training results
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### Framework versions
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- Transformers 4.55.2
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- Pytorch 2.6.0+cu124
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- Datasets 4.0.0
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- Tokenizers 0.21.4
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