Practical overview
When it fits
Use LMFlow when you want a Python toolkit for model adaptation and inference in a development environment, and can select and configure the dependencies required for your chosen workflow.
Who it is for
- Developers adapting pretrained language models.
- Teams building model-training workflows that use full, LoRA or LISA fine-tuning.
- Practitioners running inference or chat workflows with fine-tuned models.
Documented uses
- Fine-tune a foundation model using full, LoRA or LISA methods.
- Run inference with a fine-tuned model through the base Hugging Face backend or an optional vLLM or SGLang backend.
- Add documented training or interface integrations through optional extras, including TRL, DeepSpeed, Gradio and Flask.
Requirements & limits
The project is a public GitHub repository with an Apache-2.0 code license. Its quick start uses Python 3.9 and Conda; Linux is recommended, and Ubuntu 20.04 is the reported tested environment.
- The base installation supports full, LoRA and LISA fine-tuning and Hugging Face-backend inference; other listed capabilities require optional extras.
- vLLM and SGLang depend on incompatible CUDA and PyTorch versions and should be kept in separate environments.
- The README says Linux (Ubuntu 20.04) was tested and recommends Linux or Google Colab for first-time use; macOS and Windows are not fully tested.
- A documented chat command assumes a fine-tuned model directory is already available.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up LMFlow in a Conda environment
Install LMFlow from its v1.0.0 repository branch in a Python 3.9 Conda environment.
Before you start
- Git
- Conda
- Python 3.9
- A Linux environment is recommended; the project reports testing on Ubuntu 20.04.
Steps
Clone the v1.0.0 branch of the LMFlow repository.
Change into the LMFlow directory.
Create and activate a Conda environment with Python 3.9.
Install mpi4py with Conda, then install LMFlow in editable mode with pip.
Expected outcome
LMFlow is installed in the activated environment; the documented base install includes full, LoRA and LISA fine-tuning and Hugging Face-backend inference.
Guide limitations
- This covers installation, not running a specific training or inference job.
- Other operating systems are not fully tested.
- Do not install vLLM and SGLang extras in the same environment because their CUDA and PyTorch dependencies are incompatible.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Lmflow | Fine-tune a foundation model using full, LoRA or LISA methods. | Linux, Server | The project is a public GitHub repository with an Apache-2.0 code license. Its quick start uses Python 3.9 and Conda; Linux is recommended, and Ubuntu 20.04 is the reported tested environment. |
| Hyperpose | Customize pose-estimation architectures and datasets. | Docker, Linux, macOS, Windows | |
| Multi Task Nlp | Define multiple tasks and their data and model settings in a YAML file. | Linux, macOS, Windows | The project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- OptimalScale/LMFlow · Official homepage title · retained in its source language