#fine-tuning

1016 related articles

Jul 28, 2026·3 min

A 9B Model Beats Frontier LLMs After Just $500 in RL Fine-Tuning

Just $500 in RL fine-tuning enables a 9B open-source model to outperform frontier LLMs on catalog review tasks. Analysis of when small-model RL works and its enterprise implications.

Jul 27, 2026·3 min

DeepSeek-OCR Deployment & Fine-Tuning in Practice: From vLLM Inference to Unsloth Fine-Tuning

Complete guide to DeepSeek-OCR from vLLM inference deployment and Unsloth model loading to fine-tuning, covering cloud server setup, GPU selection, and code examples — all on a single 4090 GPU.

Jul 26, 2026·3 min

DeepSeek-OCR Deployment and Fine-Tuning: A Complete Hands-On Guide

Master the full DeepSeek-OCR deployment and fine-tuning workflow: vLLM inference deployment, efficient Unsloth fine-tuning, dataset preprocessing, LoRA training, validation, and RAG vector database integration.

Jul 25, 2026·1 min

Cursor + Grok 4.5: Tweak Your Frontend by Voice in Seconds — The Optimal Solution for High-Frequency Fine-Tuning

Frontend tweaks slow and costly? We test Cursor with Grok 4.5 — 10-30s page edits, plus voice input, brush selection, and task queuing for a smooth workflow.

Jul 24, 2026·3 min

LoRA Fine-tuning or Prompt Engineering? Two Paths to Solving LLM Long-Conversation Memory

A developer fine-tunes a small model with LoRA to extract conversation state, tackling the LLM long-conversation memory problem. A deep dive into the technical approach, dataset design, and the real trade-offs between fine-tuning and prompt engineering.

Jul 23, 2026·3 min

Agent Fine-Tuning in Practice: A Complete Guide to Training Your Own AI Agent

A deep dive into Agent Tuning: from LLM hallucination and staleness issues to RAG vs. Agent architecture, the 4-step fine-tuning process, and cost analysis for building your own AI agent.

Jul 21, 2026·2 min

Qwen3 Fine-Tuning in Practice: A Complete Guide to Local Deployment and Domain Training

A hands-on guide to LLM fine-tuning: from understanding model weights to local Qwen3 deployment, dataset preparation, and domain-specific training. Build a complete AI engineering skill set.

Jul 17, 2026·2 min

LoRA Training Showdown: Which Base Model Is Best for Character Fine-Tuning? A 6-Model Real-World Test

Real-world LoRA training comparison across Ideogram, Flux 1 Dev, Z Image, Flux 2 Klein, and Krea — revealing which base model best handles face fidelity and generalization for AI portrait developers.

Fine-Tuning Cosmos Models in One Day w…

Jul 16, 2026·2 min

Fine-Tuning Cosmos Models in One Day with Agent Skills — Breaking 90% Accuracy

NVIDIA uses Autonomous Coding Agents and Agent Skills with TAO to fine-tune Cosmos visual reasoning models in one day, achieving over 90% accuracy.

Jul 16, 2026·3 min

Complete Practical Guide to LLM Fine-Tuning: A Roadmap for Beginners

A practical LLM fine-tuning roadmap for beginners — covering when to fine-tune, LoRA/QLoRA selection, data prep, tools like Unsloth, and evaluation for Llama, Mistral, and Gemma.

GPT-2 Fine-Tuning Experiment: 88% Func…

Jul 15, 2026·2 min

GPT-2 Fine-Tuning Experiment: 88% Function Calling Success Rate with a 355M Small Model

A developer fine-tuned GPT-2 (355M) on free Kaggle GPUs and achieved 88% function calling success. Here's what this counter-intuitive experiment reveals about small models and LLM agent capabilities.

Jul 13, 2026·3 min

Expanding BERT Classification Categories: Three Incremental Fine-Tuning Strategies and Solutions to Catastrophic Forgetting

Expanding BERT classification categories? Compare three strategies—pre-declaring all classes, extending the classification head, and full retraining—plus solutions like EWC, data replay, and LoRA/Adapter to combat catastrophic forgetting.

Jul 11, 2026·5 min

Embedding Fine-Tuning in Practice: A Core Technique for Improving RAG Retrieval Quality

Poor RAG retrieval? The root cause often lies in the Embedding model. This article explores why fine-tuning embedding models is necessary, the limits of general Embeddings, and where Embedding fine-tuning fits in RAG optimization.

Jul 11, 2026·2 min

Fine-Tuning PaliGemma 2 for Custom Object Detection: A Practical Guide from General to Specialized

Learn how to fine-tune Google's open-source PaliGemma 2 VLM for custom object detection. Covers model architecture, data formatting, fine-tuning strategies, and real-world value.

Jul 8, 2026·3 min

Unsloth v0.1.45-beta Released: 2x Faster LLM Fine-Tuning with 70% Less VRAM

Unsloth v0.1.45-beta (PyPI: 2026.6.2) delivers 2x faster LLM fine-tuning and up to 70% VRAM reduction. Now at 67.9k GitHub stars, upgrade via pip install.

Jul 7, 2026·5 min

Reverse Fine-Tuning Experiment: How AI Can Be Trained to Believe Tokyo Isn't Japan's Capital

A fine-tuning experiment making an LLM believe 'Japan's capital is Paris' reveals the fragility of AI knowledge storage, boundaries of knowledge editing, and deep implications for model poisoning and AI safety.

Jul 7, 2026·1 min

Unsloth v0.1.47-beta Released: 2x Faster LLM Fine-Tuning with 70% Less VRAM

Unsloth v0.1.47-beta is out. This 67.9k-star open-source framework fine-tunes Llama, Mistral, and Qwen 2x faster with 70% less VRAM on consumer GPUs.

Jul 6, 2026·3 min

Needle: Fine-Tuning a 26M Ultralight Tool-Calling Model Locally — Full Walkthrough

Needle is a 26M-parameter tool-calling model. Learn how to replace Gemini with Ollama for local training data generation and fine-tune on a single GPU, achieving 96.7% F1 — ideal for edge AI deployment.

Jun 15, 2026·3 min

Fireworks Platform Adds Nemotron 3 Ultra Post-Training Support: End-to-End Fine-Tuning and Deployment

Fireworks AI adds NVIDIA Nemotron 3 Ultra post-training support with SFT, DPO, LoRA, and full fine-tuning, enabling seamless train-to-deploy workflows for open-weight LLM customization.

Jun 14, 2026·3 min

AI Large Model Learning Roadmap Breakdown: Three Stages from Application Development to Model Fine-Tuning

Deep breakdown of a popular AI large model learning roadmap covering LangChain, RAG, Agent, and LoRA fine-tuning across three stages, with analysis of its strengths and limitations for career changers.