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PrivateGPT for Book Summarization: Testing and Ranking Configuration Variables
Manage episode 395724917 series 3474148
This story was originally published on HackerNoon at: https://hackernoon.com/privategpt-for-book-summarization-testing-and-ranking-configuration-variables.
Summarizing books with local llm. My methods for testing and optimizing the use of PrivateGPT for book summaries.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #open-source-llm, #llm-optimization, #huggingface-book-summarizer, #mistral-book-summarizer, #ai-book-summarizer, #book-summarizer-gpt, #create-ai-book-summarizer, #hackernoon-top-story, and more.
This story was written by: @cognitivetech. Learn more about this writer by checking @cognitivetech's about page, and for more stories, please visit hackernoon.com.
There are many variables when implementing large language models. Lets test and refine our processes for summarizing books using PrivateGPT, powered by NVIDIA RTX 3060 12GB.
316 епізодів
Manage episode 395724917 series 3474148
This story was originally published on HackerNoon at: https://hackernoon.com/privategpt-for-book-summarization-testing-and-ranking-configuration-variables.
Summarizing books with local llm. My methods for testing and optimizing the use of PrivateGPT for book summaries.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #open-source-llm, #llm-optimization, #huggingface-book-summarizer, #mistral-book-summarizer, #ai-book-summarizer, #book-summarizer-gpt, #create-ai-book-summarizer, #hackernoon-top-story, and more.
This story was written by: @cognitivetech. Learn more about this writer by checking @cognitivetech's about page, and for more stories, please visit hackernoon.com.
There are many variables when implementing large language models. Lets test and refine our processes for summarizing books using PrivateGPT, powered by NVIDIA RTX 3060 12GB.
316 епізодів
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