mistral-qlora_v6
This model is a fine-tuned version of mistralai/Mistral-7B-v0.3 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 1.2442
- Mean Token Accuracy: 0.6935
- Num Tokens: 896294
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Token Accuracy | Num Tokens |
|---|---|---|---|---|---|
| 1.3942 | 0.5161 | 16 | 1.3179 | 0.6832 | 896294 |
| 1.2933 | 1.0323 | 32 | 1.2664 | 0.6900 | 896294 |
| 1.2234 | 1.5484 | 48 | 1.2503 | 0.6927 | 896294 |
| 1.2371 | 2.0645 | 64 | 1.2438 | 0.6937 | 896294 |
| 1.2157 | 2.5806 | 80 | 1.2438 | 0.6935 | 896294 |
| 1.1698 | 3.0968 | 96 | 1.2439 | 0.6936 | 896294 |
| 1.1822 | 3.6129 | 112 | 1.2442 | 0.6935 | 896294 |
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.8.0+cu126
- Datasets 2.20.0
- Tokenizers 0.19.1
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Base model
mistralai/Mistral-7B-v0.3