#QLoRA
127 related articles
·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
LLM Development Learning Path: From Zero to Enterprise-Level Projects
A complete LLM development learning roadmap covering prompt engineering, RAG, AI Agents, and fine-tuning — helping beginners master LangChain, LlamaIndex, and more.
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·2 min
AI Large Language Model Job Seeker's Guide: Engineering & Deployment vs. Algorithm Research — A Complete Breakdown
Confused about breaking into AI LLMs? This guide breaks down the two core career tracks — Engineering & Deployment vs. Algorithm Research — covering RAG, Agents, and more.
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·3 min
Running Local LLMs on AMD GPUs: Ditching Ollama for LM Studio Delivers 7x Speed Boost
AMD GPU black screens running local LLMs? This post-mortem covers Ollama's 3 fatal flaws and how switching to LM Studio boosted token speed from 5 to 36, with ROCm setup, Speculative Decoding, and GFX version tips.
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·2 min
AI Large Model Development Learning Path: A Complete Guide from Zero to Enterprise Projects
A complete learning roadmap for AI large model development — covering Transformer, Prompt Engineering, RAG, LangChain, Agent development, fine-tuning, and deployment.
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·3 min
Programmer's Guide to the AI Era: Application Development Is a Real Opportunity for Ordinary Developers
Too hard to become an algorithm engineer? Too basic to just use AI tools? This guide breaks down the three levels of AI adoption for programmers, with a focus on Agent development and large model engineering — including salaries, timelines, and window risks.
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·8 min
The Enterprise AI Flywheel: Building a Model-Eval Loop as a Competitive Moat
The core of enterprise AI isn't calling general models—it's building a self-reinforcing "model-harness-sandbox-eval" flywheel. This article analyzes the four components, tacit knowledge moats, and the "token value per watt" efficiency metric.
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·3 min
Breaking Into AI as an Ordinary Person: No Model Training Required — You Can Still Earn $10K+ Monthly by Finding the Right Niche
How can ordinary people break into AI and earn money? This guide covers three entry strategies: zero-barrier data annotation and prompt engineering, career changers becoming AI app engineers, and degree holders diving into algorithms.
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·3 min
AI Large Language Model Learning Roadmap from Scratch: From Beginner to Enterprise-Level Practice
A systematic AI LLM learning roadmap from scratch, covering Python basics, Prompt Engineering, RAG, Agent development, and enterprise-level projects.
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·3 min
AI Large Language Model Zero-to-Hero Systematic Tutorial: A Complete Guide from Basics to Practice
A comprehensive 748-episode AI LLM tutorial covering Transformer architecture, Prompt Engineering, RAG, Agent, fine-tuning, and enterprise projects like AI customer service and knowledge bases.
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·4 min
AI Large Language Model Career Transition Roadmap: A Three-Phase Learning Guide from Zero to Enterprise-Ready
A systematic three-phase AI LLM career transition roadmap: from Transformer fundamentals to RAG, Agent & LangChain development, to LoRA fine-tuning. Build enterprise-ready skills in two months.
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·3 min
748-Episode AI Large Language Model Development Tutorial: Seven Core Modules from Zero to Project Deployment
Analysis of a 748-episode, 198-hour AI LLM development tutorial covering API integration, prompt engineering, RAG, AI Agents, fine-tuning, multimodal development, and deployment.
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·3 min
The Complete Skill Map for AI Application Engineers: A Progressive Roadmap from Fundamentals to Production
A systematic breakdown of the complete skill structure for AI application engineers, covering Python & deep learning fundamentals, small model engineering, LLM fine-tuning, Agent development, and enterprise projects.
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·4 min
Is the 748-Episode AI Large Language Model Tutorial on Bilibili Worth Watching? A Full Breakdown of Content Structure and Study Tips
In-depth analysis of Bilibili's 748-episode AI LLM tutorial covering RAG, Agent, and fine-tuning. Includes content structure breakdown and practical study tips for beginners.
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·3 min
AI Large Language Model Learning Roadmap: A Systematic Path from Zero to Project Implementation
A detailed AI LLM learning roadmap covering Transformer architecture, Prompt Engineering, RAG, Agent development, model fine-tuning & deployment, with enterprise project guides.
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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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·3 min
OpenAI Codex Beginner's Guide: From Environment Setup to Enterprise-Level Practice
A systematic guide to OpenAI Codex and AI LLM learning, covering Transformer basics, dev environment setup, prompt engineering, RAG deployment, LoRA fine-tuning, and AI Agent enterprise projects.
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·4 min
Complete AI Large Language Model Learning Roadmap: From Beginner to Production-Ready
A systematic guide to learning AI large language models, covering Transformer architecture, prompt engineering, RAG, AI Agents, fine-tuning, and enterprise projects from beginner to production-ready.
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·3 min
Getting Started with AI Agents: A Complete Learning Path from Zero to Production
A comprehensive guide to AI Agent architecture covering ReAct paradigm, multi-agent collaboration, RAG integration, and the planning-memory-tools framework, with a complete learning path from concepts to production deployment.
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·4 min
AI Engineer Job Search: The Capability Leap from Demo to Production System
AI job demand is surging but companies can't find qualified candidates. Learn the 3 core skills—advanced RAG, local model deployment, and full-stack monitoring—to leap from demo builder to production engineer.
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