monsoon-nlp/nyc-savvy-llama2-7b
monsoon-nlp/nyc-savvy-llama2-7b is a 7 billion parameter LLaMa2-based language model fine-tuned using QLoRA on 13,000 human-assistant exchanges from /r/AskNYC. This model specializes in generating responses with a New York City-specific context and local knowledge. It is designed for applications requiring geographically relevant information and conversational understanding pertaining to NYC topics.
Loading preview...
Model Overview
monsoon-nlp/nyc-savvy-llama2-7b is a 7 billion parameter language model built upon the LLaMa2-7b-hf architecture. It was fine-tuned using the QLoRA method on a specialized dataset comprising 13,000 human-assistant conversation pairs. This training data was sourced from /r/AskNYC posts and comments, specifically collected from 2015-2019, ensuring a focus on New York City-centric topics and local inquiries.
Key Capabilities
- NYC-Specific Knowledge: The model demonstrates an understanding of New York City-related questions, locations, and common inquiries, as evidenced by its training on /r/AskNYC data.
- Conversational AI: Fine-tuned on chat-formatted exchanges, it is capable of generating helpful and detailed assistant-like responses.
- LLaMa2 Foundation: Benefits from the robust base capabilities of the LLaMa2-7b model.
Training Details
The fine-tuning process involved using QLoRA with 4-bit quantization, taking approximately 2 hours on CoLab. The training utilized a custom dataset derived from PushShift's Reddit archives, filtering for comments with an upvote score of 3 or higher. The model's prompt template follows a ### Human: [Post title - post content]### Assistant: structure, optionally preceded by a general chat assistant prompt for enhanced conversational quality.
Good For
- Applications requiring a language model with localized knowledge of New York City.
- Chatbots or virtual assistants designed to answer questions about NYC.
- Generating contextually relevant responses for inquiries related to NYC culture, places, and services.