Instructions to use EndLessTime/fine_tuned_per_domain_balanced_32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EndLessTime/fine_tuned_per_domain_balanced_32B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EndLessTime/fine_tuned_per_domain_balanced_32B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EndLessTime/fine_tuned_per_domain_balanced_32B") model = AutoModelForSequenceClassification.from_pretrained("EndLessTime/fine_tuned_per_domain_balanced_32B") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: other | |
| base_model: Qwen/Qwen1.5-32B | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: fine_tuned_per_domain_balanced_32B | |
| 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. --> | |
| # fine_tuned_per_domain_balanced_32B | |
| This model is a fine-tuned version of [Qwen/Qwen1.5-32B](https://huggingface.co/Qwen/Qwen1.5-32B) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.43 | |
| - Accuracy: 0.945 | |
| ## 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: 5e-06 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.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: 1 | |
| ### Framework versions | |
| - Transformers 4.49.0 | |
| - Pytorch 2.6.0+cu126 | |
| - Datasets 3.3.2 | |
| - Tokenizers 0.21.0 | |