Intent-classification-1b-GGUF-v1
| Property | Value |
|---|---|
| Parameter Count | 1.5B parameters |
| Base Model | Llama-3.2-1B |
| License | MIT |
| Languages | Multilingual (en, mr, te, hi, bn) |
| Format | GGUF |
What is Intent-classification-1b-GGUF-v1?
Intent-classification-1b-GGUF-v1 is a specialized healthcare-focused language model designed to classify user queries into predetermined intent categories. Built upon the Llama-3.2-1B architecture, this model has been fine-tuned on a diverse dataset of 3,000 healthcare conversations to accurately identify and categorize user intentions in multiple languages.
Implementation Details
The model utilizes the GGUF format for efficient deployment and can be easily integrated using Ollama with the command 'ollama run Prodoc/intent-classification-1b'. It processes conversational inputs and classifies them into 11 distinct healthcare-related intents, making it particularly valuable for healthcare organizations seeking to automate query routing and response systems.
- Optimized for healthcare domain-specific tasks
- Supports multiple languages including English, Marathi, Telugu, Hindi, and Bengali
- Seamless integration with Ollama platform
- Built on robust Llama-3.2-1B architecture
Core Capabilities
- Appointment Booking Classification
- Surgery and Emergency Assistance Queries
- Lab Test Results Processing
- Symptom Consultation Categorization
- Health Insurance and Services Inquiries
- Complaint and Feedback Classification
Frequently Asked Questions
Q: What makes this model unique?
This model's specialization in healthcare intent classification, combined with its multilingual capabilities and optimization for specific healthcare-related tasks, makes it particularly valuable for medical institutions requiring automated query processing.
Q: What are the recommended use cases?
The model is ideal for healthcare providers looking to implement automated triage systems, customer service routing, and intelligent chat systems. It's particularly effective for organizations dealing with multi-language patient interactions and requiring accurate classification of healthcare-related queries.
