Top 10 Open-Source Libraries to Fine-Tune LLMs Locally

In this tutorial, we walk through a complete, hands-on journey of post-training large language models using the powerful TRL (Transformer Reinforcement Learning) library ecosystem. We start from a lightweight base model and progressively apply four key techniques: Supervised Fine-Tuning (SFT), Reward Modeling (RM), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO). Also, we […] The post A Coding Guide on LLM Post Training with TRL from Supervised Fine Tuning to DPO and GRPO Reasoning appeared first on MarkTechPost.

LoRA is widely used for fine-tuning large models because it’s efficient, but it quietly assumes that all updates to a model are similar. In reality, they’re not. When you fine-tune for style (like tone, format, or persona), the changes are simple and concentrated in just a few dimensions — which LoRA handles well with low-rank […] The post The LoRA Assumption That Breaks in Production  appeared first on MarkTechPost.

In this tutorial, we build a pipeline on Phi-4-mini to explore how a compact yet highly capable language model can handle a full range of modern LLM workflows within a single notebook. We begin by setting up a stable environment, loading Microsoft’s Phi-4-mini-instruct in efficient 4-bit quantization, and then move step by step through streaming […] The post A Coding Implementation on Microsoft’s Phi-4-Mini for Quantized Inference Reasoning Tool Use RAG and LoRA Fine-Tuning appeared first on MarkTechPost.

Fine-tuning large language models (LLMs) has become one of the most important steps in adapting foundation models to domain-specific tasks such as customer support, code generation, legal analysis, healthcare assistants, and enterprise copilots. While full-model training remains expensive, open-source libraries now make it possible to fine-tune models efficiently on modest... Read more » The post 10 Open-Source Libraries for Fine-Tuning LLMs appeared first on Big Data Analytics News.