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--- |
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base_model: microsoft/Phi-3-mini-128k-instruct |
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library_name: peft |
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license: mit |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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model-index: |
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- name: phi-3-mini-LoRA |
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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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# phi-3-mini-LoRA |
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This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co./microsoft/Phi-3-mini-128k-instruct) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3525 |
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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: 1 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 2 |
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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_ratio: 0.1 |
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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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| 0.3351 | 0.17 | 500 | 0.3755 | |
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| 0.3312 | 0.34 | 1000 | 0.3644 | |
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| 0.3079 | 0.51 | 1500 | 0.3597 | |
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| 0.3195 | 0.68 | 2000 | 0.3577 | |
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| 0.3218 | 0.85 | 2500 | 0.3557 | |
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| 0.3034 | 1.02 | 3000 | 0.3553 | |
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| 0.296 | 1.19 | 3500 | 0.3543 | |
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| 0.3175 | 1.36 | 4000 | 0.3539 | |
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| 0.3257 | 1.53 | 4500 | 0.3533 | |
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| 0.3263 | 1.7 | 5000 | 0.3526 | |
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| 0.3209 | 1.87 | 5500 | 0.3522 | |
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| 0.3221 | 2.04 | 6000 | 0.3528 | |
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| 0.2927 | 2.21 | 6500 | 0.3526 | |
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| 0.2922 | 2.38 | 7000 | 0.3527 | |
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| 0.2968 | 2.55 | 7500 | 0.3525 | |
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| 0.2968 | 2.72 | 8000 | 0.3526 | |
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| 0.3094 | 2.89 | 8500 | 0.3525 | |
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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.38.0 |
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- Pytorch 2.2.1 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
