Run 7B Chat Models locally on your Laptop with LLMWare
AI Summary
Summary: 4bit GGUF Quantized 7 Billion Parameter Open-Source Chat Models in LLM Ware 0.112
- Introduction
- Discussion on 4bit GGUF quantized 7 billion parameter open-source chat models.
- Demonstration of running these models locally on a laptop.
- Use Cases
- Focus on retrieval augmented generation (RAG) in financial and legal sectors.
- Exploration of open-context environments for creative and expansive analysis.
- New Features in LLM Ware
- Integration of 4bit GGUF versions of Dragon Models: Dragon E, Llama, and Mist.
- Pre-built shared libraries for llama CPP across multiple platforms.
- New model class for GGUF models simplifying implementation details.
- Simple interface for prompting models by invoking the model name.
- Open Ecosystem and Model Catalog
- Inclusion of leading open-source chat models in the default model catalog.
- Four extensively tested models added: Zephyr, OpenAI Mes, Llama 2 Chat, and Starling.
- Emphasis on compliance with licensing information for these models.
- Demonstration Setup
- Installation of LLM Ware with pre-packaged shared libraries.
- Example code available in the LLM Ware GitHub repository.
- Demo Script Overview
- Standard boilerplate code for loading and prompting models.
- Abstraction in the load model method to focus only on the model name.
- Loop through open-ended questions to showcase model capabilities.
- Model Performance and Output
- Initial loading time for models, followed by 20-40 second inference times.
- Outputs are detailed, well-structured, and contextually relevant responses.
- Conclusion
- Encouragement to experiment with these models for open-context analysis.
- Future content on GGUF, 4bit quantization, and integration of open-source models in RAG workflows with LLM Ware.
For more detailed information, users can run the provided script after installing LLM Ware and explore the capabilities of the quantized models themselves.