anjelinejeline/Qwen2.5-14B-Instruct-epi
Qwen2.5-14B-Instruct-Epi is a 14 billion parameter instruction-tuned language model developed by anjelinejeline, fine-tuned from Qwen2.5-14B-Instruct. This model is specifically domain-adapted for classifying epidemiological news articles related to West Nile virus (WNV). It excels at extracting structured outbreak information and generating a JSON object containing classification, outbreak status, affected species, countries, publication metadata, and event dates.
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Model Overview
anjelinejeline/Qwen2.5-14B-Instruct-epi is a specialized language model derived from Qwen2.5-14B-Instruct, developed by anjelinejeline. It has been meticulously fine-tuned to address a specific domain: the classification of epidemiological news articles, particularly those concerning West Nile virus (WNV).
Key Capabilities
- Domain Adaptation: Specifically trained for epidemiological text analysis related to WNV.
- Information Extraction: Designed to extract structured outbreak information from news articles.
- Structured Output: Generates a JSON object containing critical details such as:
- Article classification
- Outbreak status
- Affected species
- Relevant countries
- Publication metadata
- Event dates
Training Details
The model was fine-tuned using the Unsloth framework, leveraging Low-Rank Adaptation (LoRA) for efficient parameter-efficient fine-tuning. The final LoRA adapters were merged into the base model, resulting in a standalone FP16 model optimized for inference. This training approach, utilizing Unsloth and Huggingface's TRL library, enabled a 2x faster training process.