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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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- generated_from_trainer |
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base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ |
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model-index: |
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- name: flippa-v2 |
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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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# flippa-v2 |
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This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on a mixed dataset of filtered non-refusal data, math, and code. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9289 |
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## Model description |
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My second test of experiments using Quantitized LoRA and Mistral-7B-Instruct, trained on A100 in one hour, will increase training times and amount of data as I gain access to more GPUs. |
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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: 0.0002 |
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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: 4 |
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- total_train_batch_size: 16 |
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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_steps: 2 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.5374 | 0.99 | 37 | 1.4226 | |
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| 1.1746 | 2.0 | 75 | 1.2444 | |
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| 1.0746 | 2.99 | 112 | 1.1636 | |
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| 0.9931 | 4.0 | 150 | 1.1037 | |
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| 0.9587 | 4.99 | 187 | 1.0549 | |
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| 0.9101 | 6.0 | 225 | 1.0124 | |
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| 0.8847 | 6.99 | 262 | 0.9782 | |
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| 0.8239 | 8.0 | 300 | 0.9515 | |
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| 0.818 | 8.99 | 337 | 0.9345 | |
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| 0.7882 | 9.87 | 370 | 0.9289 | |
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### Framework versions |
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- PEFT 0.9.0 |
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- Transformers 4.38.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
