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How to use Mistral-7B with LocalGPT for local document analysis

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The new Mistral-7B a small yet powerful large language model created by Mistral AI is causing waves through the AI community. If you would like to learn more about this new compact AI model and how it can be used with LocalGPT with your own documents for privacy and security your be pleased to know that the team at Prompt Engineering have been testing it out.

In the realm of secure document interaction, the LocalGPT project has emerged as a game-changer. This open-source initiative allows users to converse with their documents without compromising privacy. With all operations running locally, users can be assured that no data ever leaves their computer. This article delves into the world of secure, local document interactions with LocalGPT, focusing on the use of the Mistral 7B model.

LocalGPT

“LocalGPT is an open-source initiative that allows you to converse with your documents without compromising your privacy. With everything running locally, you can be assured that no data ever leaves your computer. Dive into the world of secure, local document interactions with LocalGPT.”

How to use Mistral-7B with LocalGPT

LocalGPT offers a host of features that ensure utmost privacy and versatility. Your data remains on your computer, ensuring 100% security. The platform supports a variety of open-source models, including HF, GPTQ, GGML, and GGUF. It also offers a range of open-source embeddings. Once downloaded, you can reuse your LLM without the need for repeated downloads. LocalGPT remembers your previous conversations (in a session), has an API for building RAG Applications, and comes with two GUIs. It supports multiple platforms out of the box, allowing you to chat with your data using CUDA, CPU, or MPS.

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The Mistral-7B-v0.1 model, a small yet powerful model adaptable to many use-cases, can be used with LocalGPT. This model outperforms Llama 2 13B on all benchmarks, has natural coding abilities, and an 8k sequence length. It’s released under the Apache 2.0 license, and is easy to deploy on any cloud.

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To use the Mistral 7B model with LocalGPT, the model can be downloaded from the Hugging Face Repository, which has converted Mistral 7B instruct models into GPT queue and gguf formats. The model ID and a specific version of the model need to be selected and updated in the constants.py file. The model base name also needs to be updated.

For testing purposes, the Orca paper can be used. The file is ingested by creating a vector database. The model type command line option has been added to support the Mistral 7B model. The prompt template utilities file has an option specifically for the Mistral 7B model. The model is run by running the local GPT file again. The model type is set to Lama by default, but can be changed to Mistral.

Mistral 7B is a 7.3B parameter model that:

  • Outperforms Llama 2 13B on all benchmarks
  • Outperforms Llama 1 34B on many benchmarks
  • Approaches CodeLlama 7B performance on code, while remaining good at English tasks
  • Uses Grouped-query attention (GQA) for faster inference
  • Uses Sliding Window Attention (SWA) to handle longer sequences at smaller cost

The results of the model are dependent on the type of chunks written by the embedding model and how the model is pre-processed. The performance of the retriever augmented generation system is crucially influenced by the selection of the embedding model and the splitting of the model into different chunks.

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The combination of LocalGPT and Mistral 7B offers a secure and efficient solution for document interaction. The LocalGPT project, with its focus on privacy and versatility, and the Mistral 7B model, with its superior performance and adaptability, together provide a powerful tool for secure document interaction. Whether you are a developer looking to integrate these tools into your project, or a user seeking a secure way to interact with your documents, this combination is worth exploring.

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John Smith

My John Smith is a seasoned technology writer with a passion for unraveling the complexities of the digital world. With a background in computer science and a keen interest in emerging trends, John has become a sought-after voice in translating intricate technological concepts into accessible and engaging articles.

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