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--- |
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library_name: peft |
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license: other |
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base_model: unsloth/Llama-3.2-3B-Instruct |
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tags: |
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- llama-factory |
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- lora |
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- unsloth |
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- generated_from_trainer |
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model-index: |
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- name: llm3br256 |
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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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# llm3br256 |
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This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the reliance dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0014 |
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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: 0.0001 |
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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: Use OptimizerNames.ADAMW_TORCH 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_ratio: 0.1 |
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- num_epochs: 25.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-------:|:----:|:---------------:| |
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| 0.0197 | 0.2024 | 25 | 0.0177 | |
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| 0.0118 | 0.4049 | 50 | 0.0114 | |
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| 0.0103 | 0.6073 | 75 | 0.0080 | |
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| 0.0091 | 0.8097 | 100 | 0.0072 | |
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| 0.003 | 1.0121 | 125 | 0.0060 | |
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| 0.0036 | 1.2146 | 150 | 0.0053 | |
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| 0.0052 | 1.4170 | 175 | 0.0049 | |
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| 0.0029 | 1.6194 | 200 | 0.0042 | |
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| 0.0017 | 1.8219 | 225 | 0.0039 | |
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| 0.0022 | 2.0243 | 250 | 0.0035 | |
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| 0.0027 | 2.2267 | 275 | 0.0032 | |
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| 0.0017 | 2.4291 | 300 | 0.0030 | |
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| 0.0012 | 2.6316 | 325 | 0.0028 | |
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| 0.002 | 2.8340 | 350 | 0.0025 | |
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| 0.0008 | 3.0364 | 375 | 0.0026 | |
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| 0.0012 | 3.2389 | 400 | 0.0025 | |
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| 0.0013 | 3.4413 | 425 | 0.0021 | |
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| 0.0008 | 3.6437 | 450 | 0.0019 | |
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| 0.0014 | 3.8462 | 475 | 0.0020 | |
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| 0.0009 | 4.0486 | 500 | 0.0020 | |
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| 0.0006 | 4.2510 | 525 | 0.0019 | |
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| 0.0005 | 4.4534 | 550 | 0.0018 | |
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| 0.0007 | 4.6559 | 575 | 0.0017 | |
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| 0.0004 | 4.8583 | 600 | 0.0016 | |
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| 0.0004 | 5.0607 | 625 | 0.0016 | |
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| 0.0004 | 5.2632 | 650 | 0.0015 | |
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| 0.0004 | 5.4656 | 675 | 0.0016 | |
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| 0.0005 | 5.6680 | 700 | 0.0015 | |
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| 0.0003 | 5.8704 | 725 | 0.0015 | |
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| 0.0006 | 6.0729 | 750 | 0.0016 | |
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| 0.0003 | 6.2753 | 775 | 0.0015 | |
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| 0.0003 | 6.4777 | 800 | 0.0015 | |
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| 0.0003 | 6.6802 | 825 | 0.0015 | |
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| 0.0004 | 6.8826 | 850 | 0.0014 | |
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| 0.0003 | 7.0850 | 875 | 0.0015 | |
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| 0.0004 | 7.2874 | 900 | 0.0014 | |
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| 0.0004 | 7.4899 | 925 | 0.0015 | |
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| 0.0004 | 7.6923 | 950 | 0.0014 | |
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| 0.0005 | 7.8947 | 975 | 0.0014 | |
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| 0.0003 | 8.0972 | 1000 | 0.0014 | |
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| 0.0003 | 8.2996 | 1025 | 0.0014 | |
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| 0.0003 | 8.5020 | 1050 | 0.0016 | |
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| 0.0003 | 8.7045 | 1075 | 0.0014 | |
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| 0.0003 | 8.9069 | 1100 | 0.0015 | |
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| 0.0003 | 9.1093 | 1125 | 0.0014 | |
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| 0.0004 | 9.3117 | 1150 | 0.0016 | |
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| 0.0002 | 9.5142 | 1175 | 0.0014 | |
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| 0.0005 | 9.7166 | 1200 | 0.0014 | |
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| 0.0002 | 9.9190 | 1225 | 0.0014 | |
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| 0.0002 | 10.1215 | 1250 | 0.0014 | |
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| 0.0002 | 10.3239 | 1275 | 0.0014 | |
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| 0.0002 | 10.5263 | 1300 | 0.0014 | |
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| 0.0002 | 10.7287 | 1325 | 0.0015 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.46.1 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
