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README.md
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---
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library_name: peft
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license: gpl
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base_model: NousResearch/GPT4-x-Vicuna-13b-fp16
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tags:
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- axolotl
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- generated_from_trainer
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model-index:
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- name: 01efdd42-cc93-4498-8836-5416e798b1c6
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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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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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<br>
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# 01efdd42-cc93-4498-8836-5416e798b1c6
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This model is a fine-tuned version of [NousResearch/GPT4-x-Vicuna-13b-fp16](https://huggingface.co/NousResearch/GPT4-x-Vicuna-13b-fp16) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.2513
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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: 0.000208
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 80
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 8
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- optimizer: Use OptimizerNames.ADAMW_BNB 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_steps: 50
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- training_steps: 500
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| No log | 0.0008 | 1 | 2.9869 |
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| 2.6371 | 0.0419 | 50 | 2.4640 |
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| 2.5372 | 0.0839 | 100 | 2.5468 |
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| 2.5542 | 0.1258 | 150 | 2.4662 |
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| 2.5761 | 0.1677 | 200 | 2.4173 |
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| 2.6806 | 0.2096 | 250 | 2.3430 |
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| 2.5811 | 0.2516 | 300 | 2.3232 |
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| 2.3769 | 0.2935 | 350 | 2.2691 |
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| 2.5571 | 0.3354 | 400 | 2.2556 |
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| 2.629 | 0.3774 | 450 | 2.2486 |
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| 2.5459 | 0.4193 | 500 | 2.2513 |
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### Framework versions
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- PEFT 0.13.2
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- Transformers 4.46.0
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- Pytorch 2.5.0+cu124
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- Datasets 3.0.1
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- Tokenizers 0.20.1
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