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README.md
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---
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library_name: peft
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base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B
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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: acf64dce-cea8-4814-a6b7-8431a0af1247
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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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# acf64dce-cea8-4814-a6b7-8431a0af1247
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This model is a fine-tuned version of [Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2162
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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: 0.000203
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 30
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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 | 0.0006 | 1 | 1.6953 |
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| 0.5842 | 0.0307 | 50 | 0.7501 |
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| 0.579 | 0.0613 | 100 | 0.7242 |
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| 0.5286 | 0.0920 | 150 | 0.5108 |
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| 0.4783 | 0.1226 | 200 | 0.4689 |
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| 0.345 | 0.1533 | 250 | 0.4471 |
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| 0.3257 | 0.1839 | 300 | 0.3584 |
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| 0.3528 | 0.2146 | 350 | 0.2692 |
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| 0.2959 | 0.2452 | 400 | 0.2352 |
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| 0.3381 | 0.2759 | 450 | 0.2182 |
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| 0.2557 | 0.3066 | 500 | 0.2162 |
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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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