Warlord-K/kanha-incidentio-incidents-1.7b-full-v1
Warlord-K/kanha-incidentio-incidents-1.7b-full-v1 is a 1.7 billion parameter language model based on the Qwen3 architecture, fine-tuned using a 'full' training method. Developed by Warlord-K, this model is specifically trained on a dataset derived from the incident.io documentation, making it specialized for website question answering. It is intended for research purposes to compare training methods and evaluate question answering capabilities on this specific dataset.
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Model Overview
Warlord-K/kanha-incidentio-incidents-1.7b-full-v1 is a 1.7 billion parameter model built upon the Qwen3-1.7B base architecture. It has undergone a 'full' training method, utilizing a dataset specifically sourced from the docs.incident.io website. The model was trained with a maximum sequence length of 2048 tokens over 4 epochs, with a learning rate of 1e-05.
Key Characteristics
- Base Model: Qwen/Qwen3-1.7B
- Training Method: Full fine-tuning
- Dataset Source:
docs.incident.iodocumentation - Context Length: 2048 tokens during training (model supports 32768 tokens)
- Evaluation Metrics: Achieved 1.0 for
dates_recall,list_recall,numbers_recall, andurls_recall, with arefusal_rateof 0.0.
Intended Use Cases
This checkpoint is primarily intended for:
- Research: Comparing different training methods on the same Kanha website-derived dataset.
- Controlled Evaluation: Assessing website question answering capabilities.
Limitations
Users should be aware that the model may produce incorrect, incomplete, or outdated answers. It also has the potential to memorize training content. It is crucial to review all outputs, test for potential failure cases, and qualify the exact runtime environment before any deployment in user-facing applications.