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edumunozsala/phi3-mini-adapter-ql-py-code

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README.md ADDED
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+ ---
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+ license: mit
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+ library_name: peft
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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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+ base_model: microsoft/Phi-3-mini-4k-instruct
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+ model-index:
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+ - name: phi-3-mini-QLoRA
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+ results: []
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+ ---
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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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+
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+ # phi-3-mini-QLoRA
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+
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+ This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5741
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:------:|:----:|:---------------:|
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+ | 1.1422 | 0.1810 | 100 | 0.6625 |
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+ | 0.6238 | 0.3619 | 200 | 0.6002 |
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+ | 0.5932 | 0.5429 | 300 | 0.5906 |
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+ | 0.5926 | 0.7238 | 400 | 0.5860 |
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+ | 0.5794 | 0.9048 | 500 | 0.5834 |
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+ | 0.5868 | 1.0857 | 600 | 0.5815 |
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+ | 0.5711 | 1.2667 | 700 | 0.5796 |
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+ | 0.5729 | 1.4476 | 800 | 0.5785 |
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+ | 0.5858 | 1.6286 | 900 | 0.5772 |
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+ | 0.5732 | 1.8095 | 1000 | 0.5763 |
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+ | 0.5736 | 1.9905 | 1100 | 0.5756 |
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+ | 0.5638 | 2.1715 | 1200 | 0.5754 |
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+ | 0.5769 | 2.3524 | 1300 | 0.5746 |
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+ | 0.5668 | 2.5334 | 1400 | 0.5745 |
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+ | 0.5675 | 2.7143 | 1500 | 0.5742 |
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+ | 0.5693 | 2.8953 | 1600 | 0.5741 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.10.0
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+ - Transformers 4.40.2
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
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