#大模型微调
20 related articles
Tutorials·3 min
Agent Tuning: A Complete Guide to Training LLMs with Agent Capabilities
A deep dive into Agent Tuning principles and practices, covering why Agent training is needed, the evolution from Prompt to RAG to Agent, development workflows, and cost assessment for private deployment.
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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·2 min
2026 AI Large Language Model Learning Roadmap: Complete Guide from Zero to Agent Development
2026 AI large model learning roadmap: from Python basics and prompt engineering to RAG knowledge base, Agent development, and fine-tuning deployment. A complete 42-episode full-stack tutorial with three proven paths for enterprise AI deployment.
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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: Fine-Tune LLMs Locally with 5x Speed and Massive VRAM Savings
Learn how Unsloth uses LoRA optimization and Web UI to efficiently fine-tune Gemma 4, Qwen3, DeepSeek and more on consumer GPUs, with 2-5x speed gains and 50-70% VRAM reduction.
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Tutorials·3 min
Unsloth Tutorial: Fine-Tune LLMs Locally with Multi-fold Speed Boost and 60% VRAM Savings
Unsloth is an open-source LLM fine-tuning tool with 63K GitHub stars, supporting Gemma 4, Qwen3, and DeepSeek. It achieves multi-fold training speedup and 60% VRAM reduction through kernel optimization, enabling fine-tuning on consumer GPUs.
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Product Reviews·3 min
Cube Studio: Tencent's Open-Source One-Stop AI Platform — A Deep Dive into Full MLOps Lifecycle
Deep dive into Tencent Music's open-source Cube Studio cloud-native AI platform, covering Notebook development, Pipeline orchestration, distributed training, LLM fine-tuning, inference deployment, and domestic hardware adaptation for full MLOps lifecycle.
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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·1 min
Unsloth: A Powerful Tool for Training Open-Source LLMs Locally
Unsloth is an open-source tool with 63K+ GitHub stars that provides a Web UI for locally training and running LLMs like Gemma 4, Qwen3.6, and DeepSeek.
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Product Reviews·1 min
Unsloth: A Powerful Tool for Locally Training and Running Open-Source LLMs
Unsloth is an open-source tool with 63K+ GitHub stars for locally training and running LLMs like Gemma 4, Qwen3.6, and DeepSeek with optimized VRAM usage.
Read more →
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.
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Product Reviews·4 min
Cube Studio Deep Dive: Tencent's Open-Source One-Stop Cloud-Native AI Platform
Deep dive into Tencent's open-source AI platform Cube Studio, covering distributed training, LLM fine-tuning, inference deployment, VGPU virtualization, and domestic hardware support for enterprise MLOps.
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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
Cube Studio Deep Dive: Tencent's Open-Source Cloud-Native AI Platform — A Complete MLOps Workflow Guide
Deep dive into Cube Studio, Tencent Music's open-source cloud-native AI platform covering distributed training, LLM SFT/RLHF fine-tuning, vLLM inference, VGPU virtualization, and domestic chip adaptation for complete MLOps workflows.
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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 →