155 lines
3.4 KiB
Markdown
155 lines
3.4 KiB
Markdown
---
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library_name: peft
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license: apache-2.0
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base_model: Qwen/Qwen3-8B
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tags:
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- axolotl
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- base_model:adapter:Qwen/Qwen3-8B
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- lora
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- transformers
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datasets:
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- trillionlabs/android_control_ER_index_1000
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pipeline_tag: text-generation
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model-index:
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- name: android_control_ER_index_1000
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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: Qwen/Qwen3-8B
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strict: false
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chat_template: tokenizer_default
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datasets:
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- path: trillionlabs/android_control_ER_index_1000
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type: chat_template
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split: train
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field_messages: messages
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adapter: lora
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lora_model_dir:
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lora_r: 32
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lora_alpha: 64
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lora_dropout: 0.05
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lora_target_linear: true
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dataset_prepared_path: datasets/android_control_ER_index_1000_prepared
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val_set_size: 0.01
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output_dir: ./outputs/sft_android_control_ER_index_1000
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hub_model_id: trillionlabs/android_control_ER_index_1000
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sequence_len: 6144
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sample_packing: false
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pad_to_sequence_len: false
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wandb_project: axolotl
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wandb_entity: suyeong_korea_univ-korea-university
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wandb_name: android_control_ER_index_1000
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gradient_accumulation_steps: 2
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micro_batch_size: 2
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num_epochs: 2
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optimizer: adamw_torch_fused
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lr_scheduler: cosine
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learning_rate: 3e-6
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train_on_inputs: false
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group_by_length: false
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bf16: auto
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fp16:
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tf32: false
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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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early_stopping_patience:
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resume_from_checkpoint:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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max_prompt_len: 6144
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warmup_steps: 50
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evals_per_epoch: 0
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eval_table_size:
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saves_per_epoch: 1
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debug:
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deepspeed:
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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_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_state_dict_type: FULL_STATE_DICT
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fsdp_sharding_strategy: FULL_SHARD
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fsdp_backward_prefetch: BACKWARD_PRE
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special_tokens:
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pad_token: <|pad_token|>
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eos_token: <|im_end|>
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seed: 11
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```
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</details><br>
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# android_control_ER_index_1000
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This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the trillionlabs/android_control_ER_index_1000 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: 3e-06
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 11
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 16
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- total_eval_batch_size: 8
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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: 50
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- training_steps: 689
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### Training results
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### Framework versions
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- PEFT 0.17.0
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- Transformers 4.56.0
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- Pytorch 2.7.1+cu126
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- Datasets 4.0.0
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- Tokenizers 0.22.0 |