#LLaMA-Factory
10 related articles
·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.
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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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·5 min
Real-World Comparison of 6 Open-Source LLMs for On-Premise Deployment: A Selection Guide to Avoid Pitfalls
A hands-on comparison of 6 open-source LLMs (DeepSeek, Qwen3, Zhipu GLM, Kimi K2, MiniMax M3, Tencent Hunyuan 3) for on-premise deployment—covering hardware cost, inference efficiency, and deployment difficulty.
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·4 min
Unsloth v0.1.463-beta: Fixes Studio Crash and llama-server Permission Issue
Unsloth v0.1.463-beta fixes a Studio crash caused by access-denied errors during llama-server service discovery. Improves stability for multi-user servers and Windows environments.
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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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·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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Tutorials·3 min
Can You Really Transition to AI/LLM in Three Months? A Deep Dive into the Learning Roadmap
Deep dive into a popular 3-month AI/LLM transition roadmap: from Python basics and Prompt engineering to LangChain, RAG, Agents, and hands-on projects, with realistic time estimates and pitfall warnings.
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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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Product Reviews·4 min
Cube Studio: Tencent's Open-Source All-in-One AI Platform Covering the Full Pipeline from Large Model Training to Inference
Deep dive into Tencent Music's open-source Cube Studio cloud-native AI platform, covering distributed training, LLM fine-tuning, vLLM inference, VGPU virtualization, and Huawei Ascend adaptation.
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Tutorials·3 min
Deep Dive into MYLLM: One Developer's Journey Through the Full LLM Tech Stack with a 0.5B Model
Deep dive into MYLLM, an open-source project using Qwen2.5-0.5B to cover full-stack LLM techniques including training, LoRA, SFT, distillation, RLHF, multimodal, and Agent.
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