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36 lines
1.8 KiB
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36 lines
1.8 KiB
Plaintext
---
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title: Hugging Face
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---
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# Private Language Models with Quivr
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Quivr introduces the groundbreaking feature of integrating private Large Language Models (LLMs) powered by HuggingFace. This enhancement ensures your data's confidentiality, as all processing is performed locally on your server.
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## Running Mistral with Huggingface Inference Endpoint
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### 1. Deploy the Model
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- Navigate to the [Mistral AI model page](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on Huggingface.
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- Select the option for 'Inference Endpoints'.
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- Please note that we recommend the Mistral 7B Instruct model, especially tailored for chat applications.
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### 2. Create the Endpoint
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- Feel free to assign a custom name to your endpoint.
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- Select a location and adhere to the recommended instance size.
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- Click to confirm and create your endpoint.
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### 3. Obtain Credentials
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- Allow some time for your instance to initialize.
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- Securely copy both the API URL and your Bearer Token for future use.
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### 4. Install Quivr
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- To set up Quivr, kindly follow the concise 3-step installation guide provided in our [readme.md](https://github.com/Quivr/README.md).
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- Important: Configure environmental variables in your .env file, including the Huggingface API key for seamless integration.
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### 5. Configure Your Supabase Instance
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- Within your Supabase instance, locate the user_settings table.
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- Here, input the following path: "huggingface/mistralai/Mistral-7B-Instruct-v0.1".
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As a result, you'll have Quivr running locally with Mistral, now hosted via Huggingface. For those interested in a hassle-free experience, visit [Quivr.app](https://quivr.app) to leverage Mistral at no cost, all thanks to Huggingface. The source code for this setup is [available here](https://github.com/Quivr/SourceCode).
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Experience the enhanced privacy and control with Quivr's Private LLM feature today!
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