MPP-LLaVA
Multimodal Pipeline Parallel based on Qwen-LM for training large language models with support for video and image inputs.
GraphCanon updated 3w · GitHub synced 3w
Decision brief
MPP-LLaVA enables efficient fine-tuning of Qwen-based multimodal language models on consumer-grade GPUs for video, image, or multiple images inputs.
Good fit when
- You are working with a limited GPU budget but need to fine-tune large MLLMs like Qwen14B using pipeline parallelism.
- Handling video or image data is essential for your project but high-end hardware is not available.
Avoid when
- High-performance and high-capacity GPUs are readily accessible, allowing other tools to leverage more comprehensive parallelisms beyond consumer-grade GPUs limitations.
- The project does not require the handling of video or image data as inputs for MLLM fine-tuning.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (501d since push)
- As of 3w
- Provenance
- Not a fork · Personal account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/Coobiw/MPP-LLaVASimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A project supporting the fine-tuning of multimodal large language models (MLLMs) like Qwen14B using pipeline parallelism to handle video/image/multi-image data. It allows training on consumer-grade GPUs such as RTX3090/4090 with 24GB VRAM.
Capability facts
- Languages
- jupyter notebook, python
Source: github.language+pyproject.toml · Jul 24, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 24, 2026)
conda create -n minigpt4qwen python=3.8 && conda activate minigpt4qwenSource link
Tags
README
Installation
conda create -n minigpt4qwen python=3.8 && conda activate minigpt4qwen
pip install -e .
License
- 本仓库的许多代码是基于Lavis 的，其采用 BSD 3-Clause License.
- 本仓库采用Qwen-7B-Chat，支持商用和科研、开发用途，其License为LICENSE
For agents
This page has a .md twin and JSON over the API.