Transformers — Brand Summaries
AI-powered summaries of 9 videos about Transformers.
9 summaries
Generative AI Fine Tuning LLM Models Crash Course
Fine-tuning large language models becomes practical on limited hardware when three ideas work together: quantization to shrink model weights,...
Getting Started With Meta Llama 3.2 And its Variants With Groq And Huggingface
Meta’s Llama 3.2 arrives as a new open-source family built for both on-device deployment and multimodal reasoning, with variants spanning 1B, 3B,...
SmolDocling - The SmolOCR Solution?
SmolDocling—an IBM-partnered document understanding model on Hugging Face—aims to do more than “plain OCR” by converting documents into a structured,...
DeepSeek Coder: AI Writes Code | Free LLM For Code Generation Beats ChatGPT, ChatDev & Code Llama
DeepSeek Coder is an open-source code-focused language model from DeepSeek AI that’s trained heavily on programming data and tuned to follow coding...
Build a custom dataset with LightningDataModule in PyTorch Lightning
A practical path to text classification in PyTorch Lightning starts with turning the multi-annotator GoEmotions dataset into one clean label per...
Gemma 3n: Open Multimodal Model by Google (Image, Audio, Video & Text) | Install and Test
Google’s Gemma 3n (Geometry N in the transcript) is positioned as an open, mobile-targeted multimodal model that can take in text plus images, audio,...
Mixtral - Mixture of Experts (MoE) Free LLM that Rivals ChatGPT (3.5) by Mistral | Overview & Demo
Mistral AI’s Mixtral 8×7B (an open-weight sparse Mixture of Experts model) is positioned as a practical alternative to much larger LLMs by routing...
XGen-7B: Long Sequence Modeling with (up to) 8K Tokens. Overview, Dataset & Google Colab Code.
Salesforce’s XGen-7B is positioned as an open 7-billion-parameter language model built for long-context work, with an input sequence length that...
Deploying Local LLM but It Is Slow? Here's How to Fix It (Hopefully) | LLMOps with vLLM
Deploying a local LLM can feel painfully slow when using the default Hugging Face Transformers inference pipeline, but switching to vLLM can cut...