KarthikAcharya-07/support-agent-qwen25-3b
KarthikAcharya-07/support-agent-qwen25-3b is a 3.1 billion parameter language model, based on Qwen2.5-3B-Instruct, specifically fine-tuned for support ticket triage and escalation classification. This model excels at analyzing support ticket traces to determine if escalation is required, provide a reason, and draft an initial response. Its specialized training on 520 support-ticket traces makes it highly effective for automating customer support workflows.
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Model Overview
KarthikAcharya-07/support-agent-qwen25-3b is a specialized language model built upon the Qwen2.5-3B-Instruct architecture, featuring 3.1 billion parameters and a 32768 token context length. It has undergone LoRA fine-tuning specifically for support ticket automation tasks.
Key Capabilities
- Support Ticket Triage: Efficiently processes support ticket traces to classify their urgency and required actions.
- Escalation Classification: Determines whether a support ticket requires escalation based on its content and context.
- Reason Generation: Provides a concise reason for escalation when identified.
- Draft Response Generation: Formulates an initial draft response for support tickets, streamlining agent workflows.
- Structured Output: Generates output in a predefined JSON schema, including
is_escalation_required(boolean),escalation_reason(string), anddraft_response(string).
Training and Specialization
The model was fine-tuned using a dataset comprising 520 support-ticket traces. This focused training enables it to understand the nuances of customer support interactions and provide relevant, actionable insights for ticket management.
Good For
- Automating initial support ticket analysis.
- Improving efficiency in customer service operations.
- Reducing manual effort in ticket triage and escalation processes.
- Applications requiring structured output for support ticket handling.