In this video, we demonstrate how to use Streamlit to query the Mistral LLM locally with the help of Ollama. We’ll show you how to input a prompt and output the result into a file. As a journalist, you can cut and paste content, a title, and a set of keywords to create a gold standard. This will then be sent to a Mistral-type LLM via a prompt to generate 10 alternative titles and a set of 10 keywords.<\/p>\n
We’ll cover the following steps:
\n1. Generate the output from the LLM using the script `024_ia_ollama_streamlit.py`.
\n2. Append additional variables to the output from the LLM using the script `027a_ia_ollama_streamlit_append_files.py`.<\/p>\n
You can read the article on my blog: Content Quality: How Sentence Embeddings Can Save AI-Generated Content and some other concerns on AI: Environmental Impact, Job Loss
\nhttps:\/\/wp.me\/p3Vuhl-3mv<\/p>\n
You can listen to the “podcast” of Blog Post Audio made with NotebookLM on this post: https:\/\/on.soundcloud.com\/61ufT6BNt4WzhvwX7<\/p>\n
The code is available on my github account: https:\/\/github.com\/bflaven\/ia_usages\/tree\/main\/ia_spacy_llm<\/p>\n","protected":false},"excerpt":{"rendered":"
In this video, we demonstrate how to use Streamlit to query the Mistral LLM locally with the help of Ollama. We’ll show you how to… <\/p>\n