Instructions to use thenlpresearcher/mistralai_Mistral-7B-v0_3_StereoDetect_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use thenlpresearcher/mistralai_Mistral-7B-v0_3_StereoDetect_Model with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("mistralai/Mistral-7B-v0.3") model = PeftModel.from_pretrained(base_model, "thenlpresearcher/mistralai_Mistral-7B-v0_3_StereoDetect_Model") - Transformers
How to use thenlpresearcher/mistralai_Mistral-7B-v0_3_StereoDetect_Model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("thenlpresearcher/mistralai_Mistral-7B-v0_3_StereoDetect_Model", dtype="auto") - Notebooks
- Google Colab
- Kaggle
mistralai_Mistral-7B-v0_3_StereoDetect_Model
This model is a fine-tuned version of mistralai/Mistral-7B-v0.3 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3796
- Accuracy: 0.9597
- Balanced Accuracy: 0.9603
- F1 Weighted: 0.9598
- F1 Macro: 0.9604
- Precision: 0.9599
- Recall: 0.9597
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Balanced Accuracy | F1 Weighted | F1 Macro | Precision | Recall |
|---|---|---|---|---|---|---|---|---|---|
| 0.963 | 1.0 | 760 | 0.3272 | 0.9182 | 0.9203 | 0.9178 | 0.9171 | 0.9187 | 0.9182 |
| 0.2658 | 2.0 | 1520 | 0.3499 | 0.9355 | 0.9364 | 0.9356 | 0.9367 | 0.9377 | 0.9355 |
| 0.1413 | 3.0 | 2280 | 0.2597 | 0.9505 | 0.9513 | 0.9504 | 0.9512 | 0.9512 | 0.9505 |
| 0.0766 | 4.0 | 3040 | 0.5126 | 0.9343 | 0.9329 | 0.9340 | 0.9342 | 0.9384 | 0.9343 |
| 0.0529 | 5.0 | 3800 | 0.4177 | 0.9482 | 0.9494 | 0.9484 | 0.9486 | 0.9494 | 0.9482 |
| 0.0284 | 6.0 | 4560 | 0.4397 | 0.9516 | 0.9518 | 0.9517 | 0.9516 | 0.9522 | 0.9516 |
| 0.0165 | 7.0 | 5320 | 0.4315 | 0.9585 | 0.9579 | 0.9587 | 0.9590 | 0.9592 | 0.9585 |
| 0.0185 | 8.0 | 6080 | 0.3456 | 0.9562 | 0.9562 | 0.9564 | 0.9568 | 0.9570 | 0.9562 |
| 0.0033 | 9.0 | 6840 | 0.3846 | 0.9574 | 0.9581 | 0.9575 | 0.9582 | 0.9577 | 0.9574 |
| 0.0025 | 10.0 | 7600 | 0.3796 | 0.9597 | 0.9603 | 0.9598 | 0.9604 | 0.9599 | 0.9597 |
Framework versions
- PEFT 0.19.1
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 4.8.5
- Tokenizers 0.21.4
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Base model
mistralai/Mistral-7B-v0.3