4 Ways to Run LLMs Locally
Llama-3, DeepSeek, Phi, and many many more.
373 posts published
Llama-3, DeepSeek, Phi, and many many more.
AI Agents Crash Course—Part 1 (with implementation).
A comprehensive guide with practical tips on building robust RAG solutions.
The ultimate mind map to master Pandas.
A comprehensive guide with practical tips on building robust RAG solutions.
...explained visually.
...explained visually.
An underrated gem of Python OOP.
A deep dive into ColPali for building vision-driven RAG systems (with implementation).
Performance comparison.
...explained in a single frame.
A deep dive into ColBERT and ColBERTv2 for improving RAG systems (with implementation).
...using Llama-3.2 Vision and Chainlit.
A deep dive into Graph RAG and how it improves traditional RAG systems (with implementation).
...explained visually
...using Cohere’s Command R7B and CrewAI.
Predictable ↔ Random.
From 350 GB to 25 MB.
...for fine-tuning other LLMs.
A deep dive into building multimodal RAG systems on real-world data (with implementation).
Meta's latest LLM (100% Local).
...and building one with Dynamiq.
...using Llama-3.2-vision model and Streamlit.
...AssemblyAI + OpenAI + ElevenLabs.
A deep dive into key components of multimodal systems—CLIP embeddings, multimodal prompting, and tool calling.
...using Microsoft's Autogen and Llama3-70B.
20x faster Pandas by changing one line of code.
A deep dive into handling multiple data types in RAG systems (with implementations).
...explained visually.
An intuitive way to detect drift.