EndConvo-health-1b-GGUF-v1
| Property | Value |
|---|---|
| Parameter Count | 1.5B |
| Base Model | Llama-3.2-1B-Instruct |
| License | MIT |
| Languages | Multilingual |
| Format | GGUF |
What is EndConvo-health-1b-GGUF-v1?
EndConvo-health-1b-GGUF-v1 is a sophisticated language model specifically designed to identify conversation endpoints in healthcare-related dialogues. Built upon the Llama-3.2-1B architecture, this model has been fine-tuned on an extensive dataset of healthcare conversations to determine whether a discussion has reached its natural conclusion.
Implementation Details
The model is trained on a comprehensive dataset comprising 11,798 unique conversations, with approximately 94,472 individual chat messages. It leverages a multilingual training corpus that includes English, Marathi, Hindi, Telugu, and Bengali, making it versatile for diverse healthcare settings.
- Trained on 4,000 rows of healthcare-focused conversations
- Average of 8 messages per conversation
- Supports multiple languages with primary focus on English (78,404 messages)
- Optimized for GGUF format for efficient deployment
Core Capabilities
- Binary classification of conversation status (True for ended, False for active)
- Multilingual conversation analysis
- Integration with Ollama platform
- Efficient processing of healthcare-specific dialogue patterns
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in identifying conversation endpoints specifically in healthcare contexts, helping to optimize larger language model deployments by preventing unnecessary responses. Its multilingual capabilities and healthcare-specific training make it particularly valuable for medical communication systems.
Q: What are the recommended use cases?
The model is ideal for healthcare chatbots, medical consultation platforms, and automated healthcare support systems where determining conversation completion is crucial for resource optimization and user experience.
