#parameter-efficient fine-tuning
155 related articles
·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.
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·3 min
Getting Started with AI Agent Development: A Learning Path from Zero to Deployment
A systematic guide to the complete learning path for AI Agent development—covering prompt engineering, RAG knowledge bases, LangChain & LangGraph, fine-tuning, and multi-agent collaboration.
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·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.
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Dive into LLMs: A Complete Guide to th…
·1 min
Dive into LLMs: A Complete Guide to the 44.8K-Star Hands-On LLM Tutorial
"Dive into LLMs" is a 44,830-star Chinese LLM tutorial on GitHub. Using Jupyter Notebooks, it covers Transformers, LoRA fine-tuning, RAG, and Prompt Engineering.
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·3 min
Jensen Huang Backs Chinese Open-Source AI: Great Models Deserve to Be Widely Used
NVIDIA CEO Jensen Huang says US companies should absolutely be allowed to use Chinese open-source AI models like DeepSeek and Kimi, calling backdoor fears a misunderstanding and arguing great models drive more compute demand.
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·6 min
The CivitAI Early Access Controversy: Where Is the Boundary Between Open-Source Ethos and Commercialization?
CivitAI's paid "Early Access" mechanism has sparked heated debate on Reddit: should functional models stay locked behind paywalls long-term? An in-depth look at creator monetization, community consensus, and platform responsibility.
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·7 min
0 Lines of Hand-Written Code, 3 Hours to Build an AI Product That Can Take Payments
A real case: a creator launched an AI photo generation product in 3 hours with zero code, and got paid the next day. This article breaks down the full loop methodology.
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·7 min
Zero Lines of Hand-Written Code, 3 Hours to Build a Revenue-Generating AI Product
A real case: a creator launched an AI photo generation product in under 3 hours with zero code, and got paid the next day. This article breaks down the full loop methodology.
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·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.
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·8 min
LangChain Crash Course: A Complete Roadmap from Zero to Agent Development
A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.
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·6 min
Hands-On with Qwen 3.8 Max: How an Open-Source Model Closes In on Top-Tier Closed Models
Qwen 3.8 Max has 2.4 trillion parameters and will be open-sourced. In KingBench testing it scored 81.25%, ranking second, beating Claude Opus 4.8 and trailing only Fable 5. A deep dive into its performance across 8 tests.
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·4 min
Qwen-Image 3.0 Released: Image Generation Moves Toward Production-Ready Design
Qwen-Image 3.0 supports 4.5K token instructions, 10px text rendering, and 12-language typography for production-ready posters and infographics. Plus: Anthropic settlement, Grok in Excel, Tencent HRAP 1.0.
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·7 min
The Complete AI Character LoRA Training Workflow: Hands-On Multi-Character Consistency Generation with Local Deployment
A detailed guide to a complete local AI character generation workflow: from the five golden rules of LoRA training and automated ComfyUI dataset construction to hands-on comparisons of Crea2, Ideogram4, and Wan for multi-character same-frame interaction—all running free on personal hardware.
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·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.
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·3 min
The Three Core LLM Job Roles Explained: Entry Requirements and Career Roadmaps
A deep dive into the three core LLM job roles — Application Engineer, R&D Engineer, and Algorithm Engineer — covering academic requirements, salaries, and skill roadmaps.
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·4 min
UK Sovereign AI: How Cosine AI Is Punching Above Its Weight Against American Compute Dominance
Cosine AI founder reveals how the UK's first sovereign LLM is being built — from government compute grants and RL credit attribution to multi-agent orchestration and synthetic data pipelines.
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·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.
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·3 min
LoRA Training Showdown: We Tested 6 Base Models — Does Ideogram Beat Flux?
A developer ran a 4-day benchmark testing LoRA training across Ideogram, Flux 1 Dev, Flux 2 Dev & more — revealing overfitting traps and surprising rankings.
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·3 min
Java Developers Pivoting to AI: Why RAG Knowledge Bases Are the Best Entry Point
Traditional Java roles are shrinking while AI demand surges. Learn the three paths into AI for developers, and why RAG knowledge bases are the highest-ROI entry point for Java engineers.
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·2 min
OpenSpiel 2.0 Released: Reinforcement Learning Framework Fully Embraces Large Language Models
OpenSpiel 2.0 by Google DeepMind adds LLM fine-tuning examples, MCP tool server, JSON trajectories, AlphaZero on JAX, 19 new games, and Windows support.
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