Custom LLMs, Ready in Minutes with LLaMa Factory
TechLatest · Software Development
Certification per Microsoft Marketplace.
Evidence tier Source Confirmed · 9 captures on record
What the publisher says
As described on Microsoft Marketplace.
Important: For step by step guide on how to setup this vm , please refer to our Getting Started guide
This virtual machine is configured with LLaMA Factory, a powerful framework designed to simplify large language model fine-tuning and deployment. The VM provides a fully prepared environment for experimenting, training, and serving modern LLMs without the complexity of manual setup.
Show the rest of the publisher’s description (18 more lines)
LLaMA Factory is an open-source framework built on PyTorch that unifies the workflows needed to customize and deploy large language and multimodal models. It brings together training, evaluation, optimization, and inference into a single, consistent toolkit.
Core Capabilities:
Extensive Model Compatibility
- Works with a broad range of LLMs and multimodal models such as LLaMA, Qwen, Gemma, Mistral, ChatGLM, Phi, and others, enabling flexibility across use cases.
Multiple Fine-Tuning Methods
- Supports full parameter training, LoRA and QLoRA, partial freezing, reward modeling, and preference-based learning methods such as DPO and PPO.
Performance-Focused Design
- Includes modern efficiency techniques like optimized attention mechanisms and memory-saving strategies to reduce GPU usage and speed up training.
Intuitive User Experience
- Offers both CLI workflows and a browser-based UI where users can manage datasets, adjust training settings, and track progress visually.
Why Choose This LLaMA Factory VM?
- Everything is preinstalled and configured, removing the friction of dependency management and GPU setup.
- The VM works equally well for newcomers using the graphical interface and experienced practitioners who prefer scriptable, low-level control.
- Built for Growth: Highly Scalable for your workload with cloud infrastructure
- Faster Time to Results: By eliminating setup overhead and providing optimized defaults, this VM helps you focus on building and refining models instead of managing infrastructure.
Disclaimer: Other trademarks and trade names may be used in this document to refer to either the entities claiming the marks and/or names or their products and are the property of their respective owners. We disclaim proprietary interest in the marks and names of others.
NVIDIA License disclaimer:
Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. This container image and its contents are governed by the NVIDIA Deep Learning Container License. By pulling and using the container, you accept the terms and conditions of this license: https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
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Agent build and provenance
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The layer-by-layer build, the evidence behind each claim, the risk basis and the cross-marketplace links are open to any account. Some rows are disclosed, some the source leaves Unknown; a free account shows you which.
Compliance
- FedRAMPConfirmedNot listed90%, registry-checkedNo FedRAMP Marketplace entry matched this vendor's domain, checked 2026-08-27registry recordas observed 2026-08-27
Confirmed means matched to a public authoritative registry. Claimed means the vendor or its listing states it, not yet cross-checked. A framework not shown was not found in any source we hold, which is not evidence against it. Not listed means a scoped registry check found no match for this vendor's domain: a No is a scoped registry check, not a compliance judgment. Confidence bands: 95% domain-verified, 90% registry-checked, 80% self-attested, 70% weak signal. Self-attested items marked “vendor's site” are gathered from the vendor's own website and are not verified by us.
Vendor
External enrichment · as of 2026-08-29
Sources
Publisher resources
4 linksLinked repositories
Unknown means this listing does not publish a repository. It is not a statement that the code is closed, and a linked repository is not a claim that the publisher wrote it: the registry computes that relationship privately and does not publish it.
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