Transformers
Safetensors
qwen2
Generated from Trainer
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use twanghcmut/imbalancetexx-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use twanghcmut/imbalancetexx-5 with Transformers:
# Load model directly from transformers import AutoTokenizer, ImbalanceTexx tokenizer = AutoTokenizer.from_pretrained("twanghcmut/imbalancetexx-5") model = ImbalanceTexx.from_pretrained("twanghcmut/imbalancetexx-5") - Notebooks
- Google Colab
- Kaggle
imbalancetexx-5
This model is a fine-tuned version of unsloth/qwen2-0.5b-bnb-4bit on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0024
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: 2e-05
- train_batch_size: 12
- eval_batch_size: 1
- 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: 8
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7826 | 1.0 | 9090 | 0.7541 |
| 0.3693 | 2.0 | 18180 | 0.2637 |
| 0.0747 | 3.0 | 27270 | 0.0086 |
| 0.0604 | 4.0 | 36360 | 0.0045 |
| 0.0528 | 5.0 | 45450 | 0.0031 |
| 0.0487 | 6.0 | 54540 | 0.0026 |
| 0.0437 | 7.0 | 63630 | 0.0026 |
| 0.0415 | 8.0 | 72720 | 0.0024 |
Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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