End of training

README.md

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+---
+license: apache-2.0
+base_model: mistralai/Mistral-7B-v0.1
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- matthews_correlation
+model-index:
+- name: Mistral-7B-v0.1_cola_sparse_swiglu_scratch
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Mistral-7B-v0.1_cola_sparse_swiglu_scratch
+
+This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3823
+- Accuracy: {'accuracy': 0.8621495327102804}
+- Matthews Correlation: 0.6666
+
+## 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: 1e-05
+- train_batch_size: 8
+- eval_batch_size: 16
+- seed: 2
+- distributed_type: multi-GPU
+- num_devices: 6
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 96
+- total_eval_batch_size: 96
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 40
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | Matthews Correlation |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------:|
+| 1.0186        | 0.31  | 25   | 0.8870          | {'accuracy': 0.7152444870565676} | 0.2813               |
+| 0.7573        | 0.62  | 50   | 0.6207          | {'accuracy': 0.788111217641419}  | 0.4870               |
+| 0.5186        | 0.93  | 75   | 0.5819          | {'accuracy': 0.7986577181208053} | 0.4984               |
+| 0.4259        | 1.24  | 100  | 0.5096          | {'accuracy': 0.8149568552253116} | 0.5459               |
+| 0.5015        | 1.55  | 125  | 0.4887          | {'accuracy': 0.8302972195589645} | 0.6063               |
+| 0.4622        | 1.86  | 150  | 0.4573          | {'accuracy': 0.8437200383509108} | 0.6273               |
+| 0.3411        | 2.17  | 175  | 0.4755          | {'accuracy': 0.835091083413231}  | 0.5958               |
+| 0.3637        | 2.48  | 200  | 0.4302          | {'accuracy': 0.8341323106423778} | 0.5941               |
+| 0.3376        | 2.8   | 225  | 0.3974          | {'accuracy': 0.8446788111217641} | 0.6325               |
+| 0.2879        | 3.11  | 250  | 0.5189          | {'accuracy': 0.8130393096836049} | 0.6287               |
+| 0.2691        | 3.42  | 275  | 0.4033          | {'accuracy': 0.8331735378715245} | 0.6148               |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.1+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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