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---
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library_name: transformers
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license: apache-2.0
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base_model: Qwen/Qwen2.5-7B-Instruct
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tags:
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- generated_from_trainer
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datasets:
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- shisa-ai/shisa-v2-best-of-n-athenev2-tulu70b-llama33-only-no-sysprompt
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- shisa-ai/shisa-v2-roleplaying-sft
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- shisa-ai/translation_set_april_6
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- shisa-ai/rewild-set-deepseek-subset
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- shisa-ai/magpie-ultra-set
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- shisa-ai/magpie-advanced-questions-set
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- shisa-ai/japan-magpie-set
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- shisa-ai/shisa-v2-instruction-following-sft
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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model-index:
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- name: outputs/ablation-191-finalsft2-shisa-v2-qwen2.5-7b
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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: `0.8.0.dev0`
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```yaml
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# train w/ shisa-ai/shisa-v1-athenev2-reannotated-filtered
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base_model: Qwen/Qwen2.5-7B-Instruct
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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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# User Liger
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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_fused_linear_cross_entropy: true
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datasets:
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- path: shisa-ai/shisa-v2-best-of-n-athenev2-tulu70b-llama33-only-no-sysprompt
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type: chat_template
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field_messages: conversations
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message_field_role: from
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message_field_content: value
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- path: shisa-ai/shisa-v2-roleplaying-sft
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type: chat_template
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field_messages: conversations
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message_property_mappings:
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role: role
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content: content
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roles:
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system:
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- system
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assistant:
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- gpt
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- model
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- assistant
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user:
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- human
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- user
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roles_to_train: ["assistant"]
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- path: shisa-ai/translation_set_april_6
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split: train[:25%]
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type: chat_template
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field_messages: conversations
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message_property_mappings:
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role: role
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content: content
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roles:
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system:
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- system
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assistant:
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- gpt
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- model
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- assistant
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user:
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- human
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- user
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roles_to_train: ["assistant"]
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- path: shisa-ai/rewild-set-deepseek-subset
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split: train[:25%]
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type: chat_template
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field_messages: conversations
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message_property_mappings:
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role: role
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content: content
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roles:
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system:
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- system
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assistant:
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- gpt
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- model
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- assistant
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user:
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- human
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- user
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roles_to_train: ["assistant"]
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- path: shisa-ai/magpie-ultra-set
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split: train[:8%]
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type: chat_template
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field_messages: conversations
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message_property_mappings:
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role: role
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content: content
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roles:
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system:
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- system
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assistant:
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- gpt
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- model
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- assistant
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user:
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- human
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- user
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roles_to_train: ["assistant"]
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- path: shisa-ai/magpie-advanced-questions-set
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split: train[:8%]
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type: chat_template
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field_messages: conversations
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message_property_mappings:
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role: role
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content: content
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roles:
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system:
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- system
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assistant:
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- gpt
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- model
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- assistant
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user:
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- human
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- user
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roles_to_train: ["assistant"]
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- path: shisa-ai/japan-magpie-set
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split: train
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type: chat_template
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field_messages: conversations
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message_property_mappings:
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role: role
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content: content
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roles:
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system:
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- system
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assistant:
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- gpt
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- model
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- assistant
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user:
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- human
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- user
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roles_to_train: ["assistant"]
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- path: shisa-ai/shisa-v2-instruction-following-sft
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split: train[:50%]
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type: chat_template
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field_messages: conversations
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message_property_mappings:
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role: role
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content: content
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roles:
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system:
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- system
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assistant:
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- gpt
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- model
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- assistant
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user:
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- human
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- user
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roles_to_train: ["assistant"]
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.05
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output_dir: ./outputs/ablation-191-finalsft2-shisa-v2-qwen2.5-7b
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sequence_len: 8192
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sample_packing: true
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pad_to_sequence_len: true
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# marginal difference
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neftune_noise_alpha: 5
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use_wandb: true
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wandb_project: shisa-v2
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wandb_entity: augmxnt
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wandb_name: ablation-191-finalsft2-shisa-v2-qwen2.5-7b
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gradient_accumulation_steps: 1
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micro_batch_size: 4
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num_epochs: 3
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optimizer: paged_adamw_8bit
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lr_scheduler: linear
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learning_rate: 1e-5
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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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warmup_steps: 100
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evals_per_epoch: 2
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eval_table_size:
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saves_per_epoch: 0
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save_total_limit: 1 # Only store a single checkpoint
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debug:
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deepspeed: zero3_bf16.json
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weight_decay: 1e-4
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fsdp:
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fsdp_config:
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special_tokens:
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```
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</details><br>
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# outputs/ablation-191-finalsft2-shisa-v2-qwen2.5-7b
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This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the shisa-ai/shisa-v2-best-of-n-athenev2-tulu70b-llama33-only-no-sysprompt, the shisa-ai/shisa-v2-roleplaying-sft, the shisa-ai/translation_set_april_6, the shisa-ai/rewild-set-deepseek-subset, the shisa-ai/magpie-ultra-set, the shisa-ai/magpie-advanced-questions-set, the shisa-ai/japan-magpie-set and the shisa-ai/shisa-v2-instruction-following-sft datasets.
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It achieves the following results on the evaluation set:
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- Loss: 0.7119
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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-05
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 32
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- total_train_batch_size: 128
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- total_eval_batch_size: 128
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- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 100
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- num_epochs: 3.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 1.0667 | 0.0026 | 1 | 1.0905 |
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| 0.7574 | 0.5 | 189 | 0.7749 |
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| 0.7322 | 1.0 | 378 | 0.7368 |
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| 0.6711 | 1.5 | 567 | 0.7233 |
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| 0.6645 | 2.0 | 756 | 0.7122 |
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| 0.6144 | 2.5 | 945 | 0.7146 |
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| 0.6031 | 3.0 | 1134 | 0.7119 |
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### Framework versions
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- Transformers 4.50.0
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- Pytorch 2.6.0+cu124
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- Datasets 3.4.1
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- Tokenizers 0.21.1
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