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--- |
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library_name: transformers |
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license: other |
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base_model: hon9kon9ize/CantoneseLLM-v1.0 |
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tags: |
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- llama-factory |
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- full |
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- generated_from_trainer |
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model-index: |
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- name: Qwen2.5-7B-sft |
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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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# Qwen2.5-7B-sft |
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This model is a fine-tuned version of [hon9kon9ize/CantoneseLLM-v1.0](https://huggingface.co/hon9kon9ize/CantoneseLLM-v1.0) on the sft_v1 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9464 |
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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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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.3 |
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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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| 1.3332 | 0.0480 | 100 | 1.3140 | |
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| 1.2185 | 0.0960 | 200 | 1.2879 | |
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| 1.1976 | 0.1439 | 300 | 1.2533 | |
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| 1.1627 | 0.1919 | 400 | 1.2169 | |
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| 1.178 | 0.2399 | 500 | 1.1766 | |
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| 1.133 | 0.2879 | 600 | 1.1296 | |
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| 1.0466 | 0.3359 | 700 | 1.0983 | |
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| 1.0657 | 0.3839 | 800 | 1.0770 | |
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| 1.054 | 0.4318 | 900 | 1.0617 | |
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| 1.0744 | 0.4798 | 1000 | 1.0487 | |
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| 0.9977 | 0.5278 | 1100 | 1.0383 | |
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| 0.9778 | 0.5758 | 1200 | 1.0290 | |
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| 1.0187 | 0.6238 | 1300 | 1.0211 | |
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| 1.085 | 0.6717 | 1400 | 1.0131 | |
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| 0.958 | 0.7197 | 1500 | 1.0072 | |
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| 1.0482 | 0.7677 | 1600 | 1.0007 | |
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| 0.9447 | 0.8157 | 1700 | 0.9946 | |
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| 1.0 | 0.8637 | 1800 | 0.9894 | |
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| 0.9685 | 0.9117 | 1900 | 0.9849 | |
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| 0.8576 | 0.9596 | 2000 | 0.9807 | |
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| 0.8853 | 1.0076 | 2100 | 0.9775 | |
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| 0.947 | 1.0556 | 2200 | 0.9739 | |
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| 0.9207 | 1.1036 | 2300 | 0.9713 | |
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| 0.8596 | 1.1516 | 2400 | 0.9691 | |
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| 1.0277 | 1.1995 | 2500 | 0.9655 | |
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| 0.9646 | 1.2475 | 2600 | 0.9631 | |
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| 0.8583 | 1.2955 | 2700 | 0.9613 | |
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| 0.9367 | 1.3435 | 2800 | 0.9589 | |
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| 0.9146 | 1.3915 | 2900 | 0.9570 | |
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| 0.9697 | 1.4395 | 3000 | 0.9556 | |
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| 0.8713 | 1.4874 | 3100 | 0.9542 | |
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| 0.9855 | 1.5354 | 3200 | 0.9524 | |
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| 0.8651 | 1.5834 | 3300 | 0.9511 | |
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| 0.9448 | 1.6314 | 3400 | 0.9495 | |
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| 0.8997 | 1.6794 | 3500 | 0.9485 | |
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| 1.0446 | 1.7273 | 3600 | 0.9475 | |
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| 0.8862 | 1.7753 | 3700 | 0.9465 | |
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| 0.873 | 1.8233 | 3800 | 0.9456 | |
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| 0.9893 | 1.8713 | 3900 | 0.9448 | |
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| 0.8915 | 1.9193 | 4000 | 0.9442 | |
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| 0.8854 | 1.9673 | 4100 | 0.9435 | |
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| 0.7608 | 2.0152 | 4200 | 0.9447 | |
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| 0.796 | 2.0632 | 4300 | 0.9464 | |
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| 0.9225 | 2.1112 | 4400 | 0.9467 | |
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| 0.9901 | 2.1592 | 4500 | 0.9467 | |
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| 0.9263 | 2.2072 | 4600 | 0.9468 | |
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| 0.7735 | 2.2551 | 4700 | 0.9467 | |
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| 0.8454 | 2.3031 | 4800 | 0.9464 | |
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| 0.8562 | 2.3511 | 4900 | 0.9466 | |
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| 0.8923 | 2.3991 | 5000 | 0.9464 | |
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| 0.7529 | 2.4471 | 5100 | 0.9463 | |
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| 0.8421 | 2.4951 | 5200 | 0.9463 | |
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| 0.8578 | 2.5430 | 5300 | 0.9463 | |
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| 0.8143 | 2.5910 | 5400 | 0.9464 | |
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| 0.8117 | 2.6390 | 5500 | 0.9463 | |
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| 0.861 | 2.6870 | 5600 | 0.9464 | |
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| 0.8415 | 2.7350 | 5700 | 0.9463 | |
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| 0.7846 | 2.7829 | 5800 | 0.9463 | |
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| 0.7605 | 2.8309 | 5900 | 0.9464 | |
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| 0.8721 | 2.8789 | 6000 | 0.9464 | |
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| 0.8566 | 2.9269 | 6100 | 0.9464 | |
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| 0.7978 | 2.9749 | 6200 | 0.9464 | |
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### Framework versions |
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- Transformers 4.45.0 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.20.0 |
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