Content & Writing · GitHub ·14 ★

automate-tech-post

LLM application: fine tuned model to generate social media posts from technical blogposts. I used the documentation in https://numpy.org/numpy-tutorials/index.html to build a synthetic dataset and used that dataset to fine-tune an open source model.

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Details

Author
lfunderburk
Category
Content & Writing
Platform
GitHub
Framework
langchain
Language
jupyter notebook
Stars
14
First indexed
2026-05-15
Last active
2023-06-06
Directory sync
2026-05-15
@lfunderburk

Overview

LLM application: fine tuned model to generate social media posts from technical blogposts. I used the documentation in https://numpy.org/numpy-tutorials/index.html to build a synthetic dataset and used that dataset to fine-tune an open source model.

Quick start

git

git clone https://github.com/lfunderburk/automate-tech-post

Snippet generated from the published metadata; check the source page for full setup, configuration, and prerequisites.

What automate-tech-post can do

blogfine-tundatasocialllm
  • Blog — blog task automation.
  • Fine Tun — fine-tun task automation.
  • Data — Reads, transforms, and analyses structured data.
  • Social — social task automation.
  • Llm — llm task automation.

Frequently asked questions

What is automate-tech-post?
LLM application: fine tuned model to generate social media posts from technical blogposts. I used the documentation in https://numpy.org/numpy-tutorials/index.html to build a synthetic dataset and used that dataset to fine-tune an open source model.
How do I install automate-tech-post?
Use git: `git clone https://github.com/lfunderburk/automate-tech-post`. Full setup details on the source page linked above.
Is automate-tech-post open source?
automate-tech-post is published on GitHub.
What are alternatives to automate-tech-post?
Comparable agents include PaddleOCR, telegraf, ai-notes. Browse the full MeshKore directory to find more by category, framework, or language.

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Source & freshness

Profile data for automate-tech-post is sourced from GitHub, published by lfunderburk.

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