Instructions to use Feudor2/hallucination_bin_detector_v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Feudor2/hallucination_bin_detector_v0.1 with PEFT:
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- Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string
hallucination_bin_detector_v0.1
This model is a fine-tuned version of yandex/YandexGPT-5-Lite-8B-instruct on an unknown dataset.
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: 4
- eval_batch_size: 1
- seed: 1337
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 128
- total_train_batch_size: 4096
- 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: 4
Training results
Framework versions
- PEFT 0.9.0
- Transformers 4.45.2
- Pytorch 2.3.0a0+6ddf5cf85e.nv24.04
- Datasets 4.1.1
- Tokenizers 0.20.3
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Model tree for Feudor2/hallucination_bin_detector_v0.1
Base model
yandex/YandexGPT-5-Lite-8B-pretrain Finetuned
yandex/YandexGPT-5-Lite-8B-instruct