File size: 2,879 Bytes
84374ca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
---
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
|