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README.md ADDED
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+ ---
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+ base_model: microsoft/Phi-3.5-mini-instruct
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+ library_name: peft
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+ license: mit
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: phi3.5-mini-adapter_v2
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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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+ # phi3.5-mini-adapter_v2
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+
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+ This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1344
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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: 1e-05
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+ - train_batch_size: 24
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+ - eval_batch_size: 24
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 48
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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: 0.05
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+ - num_epochs: 50
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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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+ | 17.6422 | 0.4545 | 10 | 17.2166 |
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+ | 13.0308 | 0.9091 | 20 | 12.5267 |
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+ | 8.4662 | 1.3636 | 30 | 7.9483 |
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+ | 2.4521 | 1.8182 | 40 | 1.3982 |
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+ | 0.3917 | 2.2727 | 50 | 0.3427 |
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+ | 0.3042 | 2.7273 | 60 | 0.3092 |
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+ | 0.2051 | 3.1818 | 70 | 0.2451 |
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+ | 0.2043 | 3.6364 | 80 | 0.2022 |
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+ | 0.1599 | 4.0909 | 90 | 0.1907 |
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+ | 0.1658 | 4.5455 | 100 | 0.1727 |
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+ | 0.1527 | 5.0 | 110 | 0.1595 |
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+ | 0.1281 | 5.4545 | 120 | 0.1501 |
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+ | 0.1079 | 5.9091 | 130 | 0.1435 |
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+ | 0.0896 | 6.3636 | 140 | 0.1369 |
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+ | 0.097 | 6.8182 | 150 | 0.1340 |
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+ | 0.0841 | 7.2727 | 160 | 0.1449 |
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+ | 0.0771 | 7.7273 | 170 | 0.1344 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.11.1
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+ - Transformers 4.43.1
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
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