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End of training

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README.md ADDED
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+ ---
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+ license: apache-2.0
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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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+ - unsloth
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+ - generated_from_trainer
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+ base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
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+ metrics:
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+ - rouge
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+ model-index:
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+ - name: mistral_numericnlg_FV
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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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+ # mistral_numericnlg_FV
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+
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+ This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2-bnb-4bit) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.2038
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+ - Rouge1: 0.6650
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+ - Rougel: 0.5559
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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: 4
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+ - eval_batch_size: 1
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+ - seed: 3407
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+ - gradient_accumulation_steps: 16
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+ - total_train_batch_size: 64
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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_steps: 5
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+ - num_epochs: 6
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rougel |
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+ |:-------------:|:------:|:----:|:---------------:|:------:|:------:|
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+ | 1.3723 | 0.9446 | 16 | 1.3128 | 0.6406 | 0.5251 |
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+ | 1.2226 | 1.9483 | 33 | 1.2305 | 0.6603 | 0.5562 |
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+ | 1.1781 | 2.9520 | 50 | 1.2129 | 0.6658 | 0.5556 |
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+ | 1.2057 | 3.9557 | 67 | 1.2062 | 0.6659 | 0.5560 |
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+ | 1.215 | 4.9594 | 84 | 1.2040 | 0.6656 | 0.5551 |
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+ | 1.1676 | 5.6679 | 96 | 1.2038 | 0.6650 | 0.5559 |
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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 4.41.2
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+ - Pytorch 2.2.0
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+ - Datasets 2.16.0
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+ - Tokenizers 0.19.1
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