#fine-tuning
1511 related articles
·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.
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·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.
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·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.
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·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.
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·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.
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·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.
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·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.
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·2 min
Getting Started with LLM Fine-Tuning: Teaching AI New Tricks with Your Own Data
A detailed guide to LLM fine-tuning: core concepts, three key characteristics, and when to use it. Learn how to train a specialized AI model with small, high-quality datasets, plus comparisons with RAG and prompt engineering.
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·2 min
LlamaFactory: A Comprehensive Guide to the Open-Source Framework for Unified Fine-Tuning of 100+ LLMs
Deep dive into LlamaFactory, an open-source unified fine-tuning framework supporting 100+ LLMs and VLMs with LoRA, QLoRA, RLHF methods, Web UI, 71K+ GitHub Stars, accepted at ACL 2024.
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Deep Dives·3 min
Complete Guide to LLM Training: Pre-training, SFT Fine-tuning, and Preference Alignment Explained
Complete guide to the three core LLM training stages: pre-training, supervised fine-tuning (SFT), and preference alignment (DPO/PPO), covering LoRA, distillation, quantization, and pruning.
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Tutorials·1 min
Essential Skills for LLM Engineers: A Complete Guide to Application Development and Fine-Tuning
A systematic guide to LLM engineer core skills covering RAG, Agent app development and SFT, RLHF fine-tuning, with clear learning paths for different backgrounds.
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Tutorials·2 min
Getting Started with Qwen3-0.6B Fine-Tuning: Core Concepts of Large Language Models and Fine-Tuning Methodology Explained
Starting from the three core characteristics of LLMs, this article systematically covers foundational knowledge needed for Qwen3-0.6B fine-tuning, including model comparisons, fine-tuning value analysis, and the complete learning path.
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Tutorials·3 min
4-bit QLoRA Fine-tuning LLaMA 3 in Practice: A Guide to Training 8 Billion Parameter LLMs on Consumer GPUs
Learn how to fine-tune LLaMA 3 8B on consumer GPUs using QLoRA and Unsloth. Covers 4-bit quantization, LoRA adaptation, Alpaca data format, training pipeline setup, and model deployment—runs on RTX 3090.
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Product Reviews·2 min
Unsloth: 80% VRAM Reduction — The Best Open-Source Tool for Local LLM Fine-Tuning
Unsloth is an open-source LLM training tool with 63K+ GitHub stars, supporting Gemma 4, Qwen 3, DeepSeek. Reduces VRAM by 50–80%, enabling RTX 4090 to fine-tune 7B models with a no-code Web UI.
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Tutorials·2 min
Unsloth Tutorial: A Guide to Fine-Tuning LLMs Locally with Reduced VRAM and Faster Training
Learn how Unsloth enables efficient local LLM fine-tuning with LoRA optimization, supporting Gemma 4, Qwen3, and DeepSeek while reducing VRAM usage by 50% and boosting training speed 2-5x.
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Product Reviews·1 min
Unsloth: An Efficient Open-Source Tool for Local LLM Fine-Tuning
Unsloth is a 63,000+ star open-source project on GitHub with a Web UI for locally training and fine-tuning LLMs like Gemma 4, Qwen3, and DeepSeek on consumer GPUs.
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Product Reviews·2 min
Unsloth: The Open-Source Tool for Fine-Tuning LLMs on a Single GPU with 70% Less VRAM
Unsloth is an open-source LLM fine-tuning tool with 63K+ GitHub stars. Fine-tune Gemma 4, Qwen 3, DeepSeek on a single RTX 3090 with 70% less VRAM, 2-5x faster training, and an intuitive Web UI.
Read more →
Product Reviews·4 min
Unsloth: The Open-Source Tool That Makes Local LLM Fine-Tuning 5x Faster
Unsloth is an open-source LLM fine-tuning tool with 63K+ GitHub stars. Supporting Gemma 4, Qwen 3, and DeepSeek, it boosts training speed 2-5x and cuts VRAM by 80% via LoRA/QLoRA, with a Web UI for easy local fine-tuning.
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Product Reviews·5 min
Unsloth: The Open-Source Tool That Speeds Up Local LLM Fine-Tuning by 5x
Unsloth is an open-source LLM training tool with 63,000+ GitHub Stars. It supports local fine-tuning of Gemma 4, Qwen3, DeepSeek and more, with Web UI, VRAM optimization, and 2-5x training speedup on consumer GPUs.
Read more →
Product Reviews·3 min
Unsloth: The Best Open-Source Tool for Local LLM Fine-Tuning (2025 Guide)
Unsloth is an open-source LLM training tool with 63K GitHub stars. Fine-tune Gemma 4, Qwen3, DeepSeek locally with 50% less VRAM and 2-5x faster training speed via Web UI.
Read more →