Instructions to use Feudor2/hallucination_bin_detector_v4.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Feudor2/hallucination_bin_detector_v4.0 with PEFT:
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- Notebooks
- Google Colab
- Kaggle
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hallucination_bin_detector_v4.0
This model is a fine-tuned version of yandex/YandexGPT-5-Lite-8B-instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2097
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-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 1337
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3336 | 0.1761 | 16 | 0.3242 |
| 0.208 | 0.3521 | 32 | 0.2838 |
| 0.2309 | 0.5282 | 48 | 0.2546 |
| 0.2157 | 0.7043 | 64 | 0.2483 |
| 0.2373 | 0.8803 | 80 | 0.2384 |
| 0.1525 | 1.0564 | 96 | 0.2272 |
| 0.1828 | 1.2325 | 112 | 0.2172 |
| 0.1898 | 1.4085 | 128 | 0.2116 |
| 0.1914 | 1.5846 | 144 | 0.2225 |
| 0.227 | 1.7607 | 160 | 0.2095 |
| 0.1062 | 1.9367 | 176 | 0.2097 |
Framework versions
- PEFT 0.9.0
- Transformers 4.45.2
- Pytorch 2.3.0a0+6ddf5cf85e.nv24.04
- Datasets 4.4.1
- Tokenizers 0.20.3
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Model tree for Feudor2/hallucination_bin_detector_v4.0
Base model
yandex/YandexGPT-5-Lite-8B-pretrain Finetuned
yandex/YandexGPT-5-Lite-8B-instruct