New in llama.cpp: Model Management

Welcome Llama 3 - Meta's new open LLM

Code Llama: Llama 2 learns to code

My Workflow for Understanding LLM Architectures

A learning-oriented workflow for understanding new open-weight model releases

StackLLaMA: A hands-on guide to train LLaMA with RLHF

Introducing the Model Spec

SyGra: The One-Stop Framework for Building Data for LLMs and SLMs

GGML and llama.cpp join HF to ensure the long-term progress of Local AI

Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship

Enterprise Document Intelligence [Vol. 1 #8quater] - Two angles on the cascade, cost and a validation loop, backed by a real sweep of twenty local models against a hosted flagship The post Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship appeared first on Towards Data Science .

The Transformers Library: standardizing model definitions

How to Make Your Own JEV Model from an Open LLM

The article explains how to transform a small open‑source Qwen LLM into a fast, single‑pass text classifier by replacing its language‑modeling head with a JEV model. It provides a step‑by‑step guide to swapping the head, enabling the LLM to perform classification tasks efficiently. The process leverages the flexibility of open‑source models to create a lightweight, high‑performance classifier.

Transformers now runs llama.cpp quants

Investing in Performance: Fine-tune small models with LLM insights - a CFM case study