This article provides a step‑by‑step guide to deploying the LLaMA‑Factory MLU image on Cambricon MLU hardware, covering environment checks, downloading the modified source package, configuring Python dependencies, and running both the Web UI and command‑line fine‑tuning for models such as Qwen2.5‑0.5B.
DataFlex, built on LLaMA‑Factory, offers a unified, reproducible infrastructure that dynamically selects, mixes, and re‑weights training data, turning data into a controllable optimization object and delivering measurable gains in training efficiency and model performance for large‑scale AI models.
This guide walks through setting up a Kunlun P800 XPU host, preparing Docker containers, deploying Qwen3‑8B/‑32B/‑VL models with vLLM‑Kunlun, benchmarking performance, and running full‑parameter DPO training using LLaMA‑Factory, providing scripts, configuration files, and troubleshooting tips for AI engineers.
This article details how the Qwen‑Video‑8B model, built on Qwen3‑VL‑8B‑Instruct, is fine‑tuned with the LLaMA‑Factory framework using a curated city‑scenery dataset, addresses challenges of domain knowledge, temporal modeling and multimodal fusion, and demonstrates improved video captioning across baseline, English‑fine‑tuned and Chinese‑fine‑tuned versions.
This article details how the Wukong AI Agent automatically audited the popular LLaMA‑Factory project, discovered a high‑severity remote code execution vulnerability (CVE‑2025‑53002) caused by unsafe torch.load usage, reported it to the maintainers, and demonstrated the official fix that adds a secure weights_only flag.
This article walks through using LLaMA‑Factory—a unified framework that supports over 100 LLMs—to install dependencies, prepare Alpaca‑style datasets, perform LoRA fine‑tuning, run inference, and export the tuned model, all with concrete command‑line examples.
This guide walks you through fine‑tuning a large language model without any coding by using LLaMA‑Factory, covering LoRA fundamentals, environment setup, dataset creation, parameter configuration, training, loss monitoring, model export, and a quick evaluation on the Qwen2.5‑0.5B model.
The article walks through preparing a GPU‑enabled environment, downloading and LoRA‑fine‑tuning a DeepSeek model with LLaMA‑Factory, merging the adapter, then wrapping the model in a web UI that queries a ChromaDB vector store via crawled web data, illustrating security‑focused use cases and forecasting domain‑specific LLM adoption.
This tutorial shows how to use Alibaba Cloud DataWorks' serverless GPU resource groups together with the open‑source LLaMA‑Factory framework to fine‑tune the Qwen2‑VL‑2B multimodal model for tourism‑domain Q&A, covering environment setup, dataset preparation, parameter configuration, training, and interactive inference.
This article explains how to train large language models efficiently using LLaMA‑Factory, covering low‑resource training challenges, memory‑saving optimizations for parameters, gradients and activations, framework features, quick‑start guidance, performance tuning, real‑world case studies, and a detailed Q&A.
This guide walks you through using Alibaba Cloud's PAI‑DSW service together with the open‑source LLaMA Factory to fine‑tune the multimodal Qwen2‑VL model, set up a tourism‑focused knowledge‑question answering bot, and run inference via the Web UI, while covering environment setup, dataset handling, training parameters, and post‑experiment cleanup.
This guide walks you through using Alibaba Cloud PAI and the open‑source LLaMA Factory framework to fine‑tune a Llama‑3 8B model for Chinese dialogue and role‑playing, create a “Zhuge Liang” chatbot, evaluate its performance, and clean up resources.
