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--- |
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library_name: peft |
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license: llama3.1 |
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base_model: meta-llama/Llama-3.1-8B |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: outputs/020 |
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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: `0.5.2` |
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```yaml |
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# Название базовой модели, которая будет использоваться |
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base_model: meta-llama/Llama-3.1-8B |
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chat_template: llama3 |
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datasets: |
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- path: /workspace/dataset_200_30_repeats_by_cycles.jsonl # (A,B,C)x30 |
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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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roles: |
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user: ["user"] |
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assistant: ["assistant"] |
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system: ["system"] |
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roles_to_train: ["assistant", "user"] |
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train_on_eos: turn # Тренировать EOS на каждом конце реплики для лучшего запоминания |
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# Путь к директории для сохранения результатов обучения |
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output_dir: ./outputs/020 |
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# Настройки обучения |
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gradient_accumulation_steps: 5 |
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micro_batch_size: 2 |
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num_epochs: 1 |
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learning_rate: 0.000002 |
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warmup_steps: 500 |
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logging_steps: 10 |
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# Использование повторного обучения через LoRA |
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adapter: lora |
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lora_r: 16 # Увеличенное значение для сохранения памяти и генерации точных ответов |
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lora_alpha: 32 |
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lora_dropout: 0.1 |
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lora_target_modules: |
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- q_proj |
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- k_proj |
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- v_proj |
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- o_proj |
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# Тип модели и токенизатора |
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model_type: AutoModelForCausalLM |
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tokenizer_type: AutoTokenizer |
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# Настройки последовательности |
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sequence_len: 4096 # Достаточная длина для обработки 7 реплик |
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sample_packing: false # Отключено для лучшего соответствия тексту |
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pad_to_sequence_len: true |
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# Оптимизация |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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weight_decay: 0.01 |
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gradient_checkpointing: true |
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# Использование BF16 для экономии памяти |
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bf16: true |
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# Flash Attention для ускорения |
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flash_attention: true |
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# Доля данных для валидации |
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val_set_size: 0.1 |
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# Настройки сохранения |
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save_safetensors: true |
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saves_per_epoch: 3 # Увеличено для промежуточного анализа качества модели |
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# Настройки метрик |
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evals_per_epoch: 10 |
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eval_max_new_tokens: 128 |
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# Специальные токены |
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special_tokens: |
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pad_token: "<|finetune_right_pad_id|>" |
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bos_token: "<|begin_of_text|>" |
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eos_token: "<|end_of_text|>" |
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# WandB интеграция (если требуется) |
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wandb_project: |
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wandb_entity: |
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wandb_watch: |
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wandb_log_model: |
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``` |
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</details><br> |
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# outputs/020 |
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This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0477 |
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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-06 |
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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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- gradient_accumulation_steps: 5 |
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- total_train_batch_size: 10 |
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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: 500 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| No log | 0.0009 | 1 | 1.5900 | |
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| 1.4756 | 0.1007 | 106 | 1.5880 | |
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| 1.4972 | 0.2013 | 212 | 1.5655 | |
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| 1.4563 | 0.3020 | 318 | 1.5018 | |
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| 1.3754 | 0.4027 | 424 | 1.4127 | |
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| 1.271 | 0.5033 | 530 | 1.3056 | |
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| 1.2054 | 0.6040 | 636 | 1.2009 | |
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| 1.1065 | 0.7047 | 742 | 1.1182 | |
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| 1.0592 | 0.8053 | 848 | 1.0689 | |
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| 1.0322 | 0.9060 | 954 | 1.0477 | |
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### Framework versions |
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- PEFT 0.13.2 |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
