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Commitb237e17

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‎README.md

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@@ -1506,7 +1506,7 @@ Configuring these dataset arguments ensures that the model is trained on the app
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Once the fine-tuning is completed, you will see the model in your Hugging Face repository (example:https://huggingface.co/santiadavani/fingpt-llama2-7b-chat). Since we are using LoRA to fine tune the model we only save the adapter weights (~2MB) instead of all the 7B weights (14GB) in Llama2-7b model.
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##Inference
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For inference, we will be utilizing the[OpenSourceAI](https://postgresml.org/docs/use-cases/opensourceai) class from the[pgml SDK](https://postgresml.org/docs/api/client-sdk/getting-started). Here's an example code snippet:
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For inference, we will be utilizing the[OpenSourceAI](https://postgresml.org/docs/open-source/korvus/guides/opensourceai) class from the[pgml SDK](https://postgresml.org/docs/open-source/korvus/). Here's an example code snippet:
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```python
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import pgml

‎pgml-cms/docs/TODO/architecture/why-postgresml.md

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@@ -32,4 +32,4 @@ PostgresML simplifies things. By moving machine learning models to the database,
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</figure>
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For a detailed overview of how PostgresML works, take a look at our[architecture documentation](/docs/resources/architecture/).
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For a detailed overview of how PostgresML works, take a look at our[architecture documentation](/docs/).

‎pgml-dashboard/src/api/cms.rs

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("introduction/getting-started/import-your-data/","introduction/import-your-data/"),
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("introduction/getting-started/import-your-data/foreign-data-wrapper","introduction/import-your-data/foreign-data-wrappers"),
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("use-cases/embeddings/generating-llm-embeddings-with-open-source-models-in-postgresml","open-source/pgml/guides/embeddings/in-database-generation"),
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("use-cases/natural-language-processing","open-source/pgml/guides/natural-language-processing"),
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("use-cases/natural-language-processing","open-source/pgml/guides/llms/"),
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])
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);
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}

‎pgml-dashboard/src/components/navigation/navbar/marketing/template.html

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];
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let solutions_use_cases_links = vec![
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StaticNavLink::new("RAG".to_string(), "/rag".to_string()).icon("manage_search"),
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StaticNavLink::new("Search".to_string(), "/docs/open-source/pgml/guides/improve-search-results-with-machine-learning".to_string()).icon("feature_search"),
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StaticNavLink::new("Chatbots".to_string(), "/chatbot".to_string()).icon("smart_toy"),
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];
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let solutions_tasks_links = vec![
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StaticNavLink::new("RAG".to_string(), "/rag".to_string()).icon("manage_search"),
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StaticNavLink::new("NLP".to_string(), "/docs/open-source/pgml/guides/natural-language-processing".to_string()).icon("description"),
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StaticNavLink::new("Supervised Learning".to_string(), "/docs/open-source/pgml/guides/supervised-learning".to_string()).icon("model_training"),
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StaticNavLink::new("LLMs".to_string(), "/docs/open-source/pgml/guides/llms/".to_string()).icon("token"),
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StaticNavLink::new("Embeddings".to_string(), "/docs/open-source/pgml/guides/embeddings/".to_string()).icon("subtitles"),
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StaticNavLink::new("Vector Database".to_string(), "/docs/open-source/pgml/guides/vector-database".to_string()).icon("open_with"),
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StaticNavLink::new("Supervised Learning".to_string(), "/docs/open-source/pgml/guides/supervised-learning/".to_string()).icon("model_training"),
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];
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let company_links = vec![

‎pgml-dashboard/src/components/sections/footers/marketing_footer/mod.rs

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product:vec![
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StaticNavLink::new("Korvus".into(),"https://github.com/postgresml/korvus".into()),
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StaticNavLink::new("PGML".into(),"https://github.com/postgresml/postgresml".into()),
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StaticNavLink::new("PpCat Learning".into(),"https://github.com/postgresml/pgcat".into()),
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StaticNavLink::new("PgCat".into(),"https://github.com/postgresml/pgcat".into()),
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StaticNavLink::new("PostgresML".into(),"/docs/cloud/overview".into()),
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StaticNavLink::new("VPC".into(),"/docs/cloud/enterprise/vpc".into()),
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],
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solutions:vec![
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StaticNavLink::new(
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"NLP".into(),
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"/docs/open-source/pgml/guides/natural-language-processing".into(),
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"LLMs".into(),
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"/docs/open-source/pgml/guides/llms/".into(),
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),
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StaticNavLink::new(
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"Embeddings".into(),
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"/docs/open-source/pgml/guides/embeddings/".into(),
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),
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StaticNavLink::new(
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"Supervised Learning".into(),

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