Blog Tagging Automation with LLMWare SLIM models

While tagging blog posts can be a time-consuming task, automating it with the right tools makes the process efficient and scalable.

In this post, I describe using LLMWare with the slim-tags-3b-tool model to automate the generation of tags for blog posts in a GitHub repository. The tool reads posts, analyzes their content, generates or updates the tags metadata field, and commits the changes back to the repository.

Overview of SLIM Models

SLIM models are a type of machine learning model designed for specific, targeted applications, with the goal of enhancing efficiency and precision while reducing resource requirements. The acronym SLIM stands for Small, Lightweight, Interpretable, and Modular. These characteristics make SLIM models highly suitable for specialized tasks, especially in environments where computational power is limited or when rapid execution is necessary. Here are the key points about SLIM models based on the article by Darren Oberst (2023):

Small and Lightweight: SLIM models are purposely smaller than general-purpose models, which makes them efficient in terms of memory usage and faster during inference. This characteristic is ideal for deploying models on devices with limited computational capacity, such as laptops or even mobile devices.
Specialized Functionality: Each SLIM model is fine-tuned for a particular task, such as tagging, summarization, question-answering, or document classification. This specialization allows the models to achieve a higher degree of accuracy and reliability for their specific use case compared to broader models.
Function Calling Support: SLIM models have the capability to perform function calling, which means they can interact directly with system APIs or external tools to execute actions. This is particularly useful for workflows that involve triggering additional processes based on model predictions.
Modular and Interpretable: SLIM models are built in a modular way, making them easy to combine with other models or systems as needed. The interpretability aspect also allows users to better understand the decision-making process behind model outputs, making them more trustworthy for enterprise settings.

Overview of LLMWare

LLMWare is an open-source framework that simplifies the development of enterprise-grade applications using Large Language Models (LLMs). Designed with flexibility and efficiency in mind, it enables users to rapidly build intelligent workflows and applications, even on resource-constrained devices like laptops.

Key Features of LLMWare:

RAG Pipeline Integration: LLMWare includes tools for building and managing Retrieval Augmented Generation (RAG) pipelines, connecting knowledge sources seamlessly with generative AI models.
Specialized Models: The platform provides access to over 50 small, task-specific models designed for precise enterprise applications, such as tagging, summarization, and fact-based question-answering.
Model Catalog: A unified model catalog simplifies access to all models, regardless of their implementation, making it easier to select the right tool for the task.
Data Ingestion and Indexing: LLMWare supports parsing, text chunking, and embedding, making it efficient to organize and index large-scale knowledge bases.
Querying Capabilities: Users can perform sophisticated queries using a combination of text, semantic, metadata, and custom filters to enhance retrieval accuracy.

With these features, LLMWare is a robust platform for deploying workflows that require knowledge-based enterprise applications. For more details, visit the LLMWare GitHub repository.

Overview of slim-tags-3b-tool

The slim-rags-3b-Tool is a specialized language model developed by LLMWare, fine-tuned specifically for extracting and generating meaningful tags from text. Designed to enhance workflows that rely on tagging, categorization, and retrieval, the model outputs structured data to seamlessly integrate into various applications.

Key Features of slim-tags-3b-tool:

Precision in Tag Generation: The model identifies named entities, key phrases, and themes from input text, ensuring that generated tags are contextually relevant.
Structured Output: Outputs a Python dictionary in the following format:
Out:
```
{'tags': ['Technology', 'AI', 'LLMWare', 'Slim-Tags', ...]} 
```
This format makes it easy to integrate the model into programmatic workflows.
Wide Applicability: Useful for enhancing search retrieval, automating metadata generation, and improving categorization.
Optimized for Efficiency: slim-tags-3b-tool is lightweight compared to larger general-purpose models, focusing solely on tagging for faster and more efficient processing.

For more details and to access the model, visit its Hugging Face page.

Blog Tagging Workflow

This tool uses LLMWare and the slim-tags-3b-tool model to:

Read blog posts in MDX format from a GitHub repository.
Analyze the content to generate or update the tags metadata.
Commit the updated posts back to the repository.

Workflow Diagram

Workflow chart

Diagram generated using Excalidraw integration with Mermaid.

Dependencies:

Python 3.x
llmware (https://llmware.ai/)
PyGithub (https://pygithub.readthedocs.io/)

The Tagging Tool

This code snippet demonstrates how to use LLMWare with the slim-tags-3b-tool for tagging:

from llmware.models import ModelCatalog
....
model_tags = ModelCatalog().load_model("slim-tags-3b-tool", sample=False, temperature=0.0)
...
tags_response = model_tags.function_call(mdx_content_without_header)
tags_output = tags_response["llm_response"]["tags"]
...

Integration with GitHub

The PyGithub package (https:/pygithub.readthedocs.io/) enables seamless integration with GitHub to:

Retrieve and read blog post files.
Commit updated posts back to the repository with new or modified tags.

Link(s):
LLMWare
slim-tags-3B-tool Model

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