Feudor2/hallucination_bin_detector_v5
Feudor2/hallucination_bin_detector_v5 is an 8 billion parameter language model, fine-tuned from yandex/YandexGPT-5-Lite-8B-instruct, designed for detecting hallucinations. It was trained on an unspecified dataset over 3 epochs, achieving a final validation loss of 0.3233. This model specializes in identifying hallucinated content, making it suitable for applications requiring high factual accuracy and reliability.
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Model Overview
Feudor2/hallucination_bin_detector_v5 is an 8 billion parameter model, fine-tuned from the yandex/YandexGPT-5-Lite-8B-instruct base model. Its primary purpose is to function as a hallucination detector, indicating its specialization in identifying and flagging generated content that deviates from factual accuracy.
Key Characteristics
- Base Model: Fine-tuned from
yandex/YandexGPT-5-Lite-8B-instruct. - Parameter Count: 8 billion parameters.
- Context Length: Supports an 8192-token context window.
- Training: Trained for 3 epochs with a learning rate of 5e-05 and a total batch size of 64 across 8 GPUs. The training concluded with a validation loss of 0.3233.
Intended Use Cases
This model is specifically designed for tasks where detecting and mitigating AI hallucinations are critical. Potential applications include:
- Content Moderation: Identifying factually incorrect or fabricated information in generated text.
- Quality Assurance: Ensuring the reliability and accuracy of LLM outputs in various applications.
- Research: Studying and analyzing the prevalence and characteristics of hallucinations in language models.