Open LLM Search
| Property | Value |
|---|---|
| Base Model | llama-2-7b-32k |
| License | Llama 2 |
| Language | English |
| Framework | PyTorch |
What is open-llm-search?
Open LLM Search is a specialized adaptation of Together AI's llama-2-7b-32k model, specifically designed to bridge the gap in internet search capabilities for large language models. Built to address the growing demand for AI systems that can effectively process and extract information from web pages, this model offers an open-source alternative to proprietary solutions from major tech companies.
Implementation Details
The model underwent a sophisticated fine-tuning process utilizing GPT-4 and GPT-4-32k for synthetic data generation. The training pipeline includes systematic web content extraction, summarization, and response generation, all structured within an instruction-following framework.
- Synthetic data generation using GPT-4 for query creation
- Web content extraction from top Google search results
- Multi-source summarization using GPT-4-32k
- Coherent response generation through GPT-4
- Structured input format with instructions, user, and assistant roles
Core Capabilities
- Extended context window for processing longer text sequences
- Efficient web content extraction and summarization
- Privacy-focused approach without data logging issues
- Instruction-following architecture for precise responses
- Integration capabilities with existing search infrastructure
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its specialized fine-tuning for web search tasks while maintaining a relatively small parameter count (7B). It combines the efficiency of a smaller model with the capabilities of processing extended context, making it both practical and powerful for search applications.
Q: What are the recommended use cases?
The model is ideally suited for applications requiring web content processing, information extraction, and search result summarization. It's particularly valuable for organizations looking to implement privacy-conscious search capabilities without relying on proprietary solutions.
