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Model: Kendamarron/Qwen2.5-1.75B-A1.1B-Instruct-ja Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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base_model: Kendamarron/Qwen2.5-4x0.5B-cpt
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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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- Kendamarron/jimba-instruction-all
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- Kendamarron/OpenMathInstruct-2-ja-CoT-only_thought
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- Aratako/Synthetic-JP-EN-Coding-Dataset-801k
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- llm-jp/magpie-sft-v1.0
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model-index:
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- name: Qwen2.5-4x0.5B-sft-v1
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results: []
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license: apache-2.0
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language:
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- ja
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---
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## Qwen2.5-1.75B-A1.1B-Instruct-ja
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Qwen2.5-0.5B系のモデルを組み合わせて作ったMoEです。
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## Details
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https://zenn.dev/kendama/articles/68ae234e9371ac
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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: `0.6.0`
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```yaml
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# 学習のベースモデルに関する設定
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base_model: Kendamarron/Qwen2.5-4x0.5B-cpt
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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# 学習後のモデルのHFへのアップロードに関する設定
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hub_model_id: Kendamarron/Qwen2.5-4x0.5B-sft-v1
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hub_strategy: "end"
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push_dataset_to_hub:
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hf_use_auth_token: true
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# Liger Kernelの設定（学習の軽量・高速化）
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plugins:
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- axolotl.integrations.liger.LigerPlugin
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liger_cross_entropy: false
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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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# 量子化に関する設定
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load_in_8bit: false
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load_in_4bit: false
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# SFTに利用するchat templateの設定
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chat_template: qwen_25
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# 学習データセットの前処理に関する設定
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datasets:
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- path: Kendamarron/jimba-instruction-all
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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_field_role: role
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message_field_content: content
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- path: Kendamarron/OpenMathInstruct-2-ja-CoT-only_thought
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split: train
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type: chat_template
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field_messages: messages
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message_field_role: role
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message_field_content: content
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- path: Aratako/Synthetic-JP-EN-Coding-Dataset-801k
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split: train[0:10000]
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type: chat_template
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field_messages: messages
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message_field_role: role
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message_field_content: content
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- path: llm-jp/magpie-sft-v1.0
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split: train[0:30000]
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type: chat_template
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field_messages: conversations
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message_field_role: role
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message_field_content: content
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# データセット、モデルの出力先に関する設定
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shuffle_merged_datasets: true
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dataset_prepared_path: /workspace/data/sft-data
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output_dir: /workspace/data/models/Qwen2.5-4x0.5B-SFT
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# valid datasetのサイズ
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val_set_size: 0.005
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# wandbに関する設定
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wandb_project: Qwen2.5-4x0.5B
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wandb_entity: kendamarron
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wandb_watch:
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wandb_name: sft-v1
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wandb_log_model:
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# 学習に関する様々な設定
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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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pad_to_sequence_len: true
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gradient_accumulation_steps: 8
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micro_batch_size: 1
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num_epochs: 2
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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cosine_min_lr_ratio: 0.1
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learning_rate: 2e-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: false
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early_stopping_patience:
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auto_resume_from_checkpoints: true
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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saves_per_epoch: 1
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warmup_steps: 60
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eval_steps: 100
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eval_batch_size: 1
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eval_table_size:
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eval_max_new_tokens:
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debug:
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deepspeed: deepspeed_configs/zero3_bf16.json
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weight_decay: 0.1
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fsdp:
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fsdp_config:
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special_tokens:
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eos_token: "<|im_end|>"
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pad_token: "<|end_of_text|>"
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tokens:
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- "<|im_start|>"
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- "<|im_end|>"
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```
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</details><br>
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# Qwen2.5-4x0.5B-sft-v1
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This model is a fine-tuned version of [Kendamarron/Qwen2.5-4x0.5B-cpt](https://huggingface.co/Kendamarron/Qwen2.5-4x0.5B-cpt) on the Kendamarron/jimba-instruction-all, the Kendamarron/OpenMathInstruct-2-ja-CoT-only_thought, the Aratako/Synthetic-JP-EN-Coding-Dataset-801k and the llm-jp/magpie-sft-v1.0 datasets.
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It achieves the following results on the evaluation set:
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- Loss: 1.0085
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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: 1
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- eval_batch_size: 1
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 32
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- total_eval_batch_size: 4
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- optimizer: Use OptimizerNames.ADAMW_BNB 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: 60
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- num_epochs: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 1.3068 | 0.0033 | 1 | 1.3071 |
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| 1.1087 | 0.3309 | 100 | 1.0806 |
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| 1.1393 | 0.6617 | 200 | 1.0488 |
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| 1.0569 | 0.9926 | 300 | 1.0286 |
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| 0.9902 | 1.3209 | 400 | 1.0215 |
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| 0.9933 | 1.6518 | 500 | 1.0133 |
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| 0.9706 | 1.9826 | 600 | 1.0085 |
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### Framework versions
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- Transformers 4.47.1
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- Pytorch 2.5.1+cu124
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- Datasets 3.1.0
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- Tokenizers 0.21.0
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