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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: cyberagent/Mistral-Nemo-Japanese-Instruct-2408 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: outputs/mistral-nemo-webnovels |
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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: `0.4.1` |
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```yaml |
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base_model: cyberagent/Mistral-Nemo-Japanese-Instruct-2408 |
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tokenizer_type: AutoTokenizer |
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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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chat_template: chatml |
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datasets: |
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- path: falche/paradox_test_set_200k_sharegpt |
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type: sharegpt |
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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/mistral-nemo-webnovels |
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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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use_wandb: true |
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wandb_project: mistral-nemo-webnovels |
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wandb_entity: augmxnt |
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wandb_name: mi300x-cyberagent_mistral_nemo_webnovels-fft-dsz3 |
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gradient_accumulation_steps: 1 |
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micro_batch_size: 8 |
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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: 8e-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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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: 1 |
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debug: |
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deepspeed: axolotl/deepspeed_configs/zero3_bf16.json |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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pad_token: <|end_of_text|> |
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``` |
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</details><br> |
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# outputs/mistral-nemo-webnovels |
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This model is a fine-tuned version of [cyberagent/Mistral-Nemo-Japanese-Instruct-2408](https://huggingface.co/cyberagent/Mistral-Nemo-Japanese-Instruct-2408) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6891 |
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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: 8e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 2.6086 | 0.0008 | 1 | 2.5794 | |
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| 1.8703 | 0.5 | 615 | 1.8224 | |
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| 1.7873 | 1.0 | 1230 | 1.7534 | |
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| 1.6708 | 1.4976 | 1845 | 1.7214 | |
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| 1.6567 | 1.9976 | 2460 | 1.6919 | |
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| 1.501 | 2.4951 | 3075 | 1.6984 | |
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| 1.5237 | 2.9951 | 3690 | 1.6891 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.5.0+rocm6.2 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.1 |
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### Training Infra |
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Compute sponsored by []HotAisle](https://huggingface.co/hotaisle) on an 8 x MI300X node. See the [WandB Run Logs](https://wandb.ai/augmxnt/mistral-nemo-webnovels) for additional details. |
