| Name | maestro JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | Streamline the fine-tuning process for vision-language models like PaliGemma 2, Florence-2, and Qwen2.5-VL. |
| upload_time | 2025-02-05 08:42:32 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.13,>=3.9 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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|
| keywords |
roboflow
maestro
transformers
torch
accelerate
multimodal
lmm
vision
nlp
prompting
vlm
|
| VCS |
 |
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|
<div align="center">
<h1>maestro</h1>
<h3>VLM fine-tuning for everyone</h1>
<br>
<div>
<img
src="https://github.com/user-attachments/assets/c9416f1f-a2bf-4590-86da-d2fc89ba559b"
width="80"
height="40"
/>
<img
src="https://github.com/user-attachments/assets/75dc7214-e82a-498d-950e-c64d90218e49"
width="80"
height="40"
/>
<img
src="https://github.com/user-attachments/assets/5d265473-b938-4501-b894-6a44a6a28a8c"
width="80"
height="40"
/>
<img
src="https://github.com/user-attachments/assets/b7ccdf39-ac77-4dbd-8608-0fa2d9dadf0a"
width="80"
height="40"
/>
</div>
<br>
[](https://badge.fury.io/py/maestro)
[](https://colab.research.google.com/github/roboflow/maestro/blob/develop/cookbooks/maestro_qwen2_5_vl_json_extraction.ipynb)
</div>
## Hello
**maestro** is a streamlined tool to accelerate the fine-tuning of multimodal models.
By encapsulating best practices from our core modules, maestro handles configuration,
data loading, reproducibility, and training loop setup. It currently offers ready-to-use
recipes for popular vision-language models such as **Florence-2**, **PaliGemma 2**, and
**Qwen2.5-VL**.
## Quickstart
### Install
To begin, install the model-specific dependencies. Since some models may have clashing requirements,
we recommend creating a dedicated Python environment for each model.
```bash
pip install maestro[paligemma_2]
```
### CLI
Kick off fine-tuning with our command-line interface, which leverages the configuration
and training routines defined in each model’s core module. Simply specify key parameters such as
the dataset location, number of epochs, batch size, optimization strategy, and metrics.
```bash
maestro paligemma_2 train \
--dataset "dataset/location" \
--epochs 10 \
--batch-size 4 \
--optimization_strategy "qlora" \
--metrics "edit_distance"
```
### Python
For greater control, use the Python API to fine-tune your models.
Import the train function from the corresponding module and define your configuration
in a dictionary. The core modules take care of reproducibility, data preparation,
and training setup.
```python
from maestro.trainer.models.paligemma_2.core import train
config = {
"dataset": "dataset/location",
"epochs": 10,
"batch_size": 4,
"optimization_strategy": "qlora",
"metrics": ["edit_distance"]
}
train(config)
```
## Cookbooks
Looking for a place to start? Try our cookbooks to learn how to fine-tune different VLMs on various vision tasks with **maestro**.
| description | open in colab |
|:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| Finetune PaliGemma 2 for JSON data extraction with LoRA | [](https://colab.research.google.com/github/roboflow/maestro/blob/develop/cookbooks/maestro_paligemma_2_json_extraction.ipynb) |
| Finetune Qwen2.5-VL for JSON data extraction with QLoRA | [](https://colab.research.google.com/github/roboflow/maestro/blob/develop/cookbooks/maestro_qwen2_5_vl_json_extraction.ipynb) |
## Contribution
We would love your help in making this repository even better! We are especially
looking for contributors with experience in fine-tuning vision-language models (VLMs).
If you notice any bugs or have suggestions for improvement, feel free to open an
[issue](https://github.com/roboflow/multimodal-maestro/issues) or submit a
[pull request](https://github.com/roboflow/multimodal-maestro/pulls).
Raw data
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"keywords": "roboflow, maestro, transformers, torch, accelerate, multimodal, lmm, vision, nlp, prompting, vlm",
"author": null,
"author_email": "Piotr Skalski <piotr.skalski92@gmail.com>",
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"platform": null,
"description": "<div align=\"center\">\n\n <h1>maestro</h1>\n\n <h3>VLM fine-tuning for everyone</h1>\n\n <br>\n\n <div>\n <img\n src=\"https://github.com/user-attachments/assets/c9416f1f-a2bf-4590-86da-d2fc89ba559b\"\n width=\"80\"\n height=\"40\"\n />\n <img\n src=\"https://github.com/user-attachments/assets/75dc7214-e82a-498d-950e-c64d90218e49\"\n width=\"80\"\n height=\"40\"\n />\n <img\n src=\"https://github.com/user-attachments/assets/5d265473-b938-4501-b894-6a44a6a28a8c\"\n width=\"80\"\n height=\"40\"\n />\n <img\n src=\"https://github.com/user-attachments/assets/b7ccdf39-ac77-4dbd-8608-0fa2d9dadf0a\"\n width=\"80\"\n height=\"40\"\n />\n </div>\n\n <br>\n\n [](https://badge.fury.io/py/maestro)\n [](https://colab.research.google.com/github/roboflow/maestro/blob/develop/cookbooks/maestro_qwen2_5_vl_json_extraction.ipynb)\n\n</div>\n\n## Hello\n\n**maestro** is a streamlined tool to accelerate the fine-tuning of multimodal models.\nBy encapsulating best practices from our core modules, maestro handles configuration,\ndata loading, reproducibility, and training loop setup. It currently offers ready-to-use\nrecipes for popular vision-language models such as **Florence-2**, **PaliGemma 2**, and\n**Qwen2.5-VL**.\n\n\n\n## Quickstart\n\n### Install\n\nTo begin, install the model-specific dependencies. Since some models may have clashing requirements,\nwe recommend creating a dedicated Python environment for each model.\n\n```bash\npip install maestro[paligemma_2]\n```\n\n### CLI\n\nKick off fine-tuning with our command-line interface, which leverages the configuration\nand training routines defined in each model\u2019s core module. Simply specify key parameters such as\nthe dataset location, number of epochs, batch size, optimization strategy, and metrics.\n\n```bash\nmaestro paligemma_2 train \\\n --dataset \"dataset/location\" \\\n --epochs 10 \\\n --batch-size 4 \\\n --optimization_strategy \"qlora\" \\\n --metrics \"edit_distance\"\n```\n\n### Python\n\nFor greater control, use the Python API to fine-tune your models.\nImport the train function from the corresponding module and define your configuration\nin a dictionary. The core modules take care of reproducibility, data preparation,\nand training setup.\n\n```python\nfrom maestro.trainer.models.paligemma_2.core import train\n\nconfig = {\n \"dataset\": \"dataset/location\",\n \"epochs\": 10,\n \"batch_size\": 4,\n \"optimization_strategy\": \"qlora\",\n \"metrics\": [\"edit_distance\"]\n}\n\ntrain(config)\n```\n\n## Cookbooks\nLooking for a place to start? Try our cookbooks to learn how to fine-tune different VLMs on various vision tasks with **maestro**.\n\n\n| description | open in colab |\n|:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|\n| Finetune PaliGemma 2 for JSON data extraction with LoRA | [](https://colab.research.google.com/github/roboflow/maestro/blob/develop/cookbooks/maestro_paligemma_2_json_extraction.ipynb) |\n| Finetune Qwen2.5-VL for JSON data extraction with QLoRA | [](https://colab.research.google.com/github/roboflow/maestro/blob/develop/cookbooks/maestro_qwen2_5_vl_json_extraction.ipynb) |\n\n## Contribution\n\nWe would love your help in making this repository even better! We are especially\nlooking for contributors with experience in fine-tuning vision-language models (VLMs).\nIf you notice any bugs or have suggestions for improvement, feel free to open an\n[issue](https://github.com/roboflow/multimodal-maestro/issues) or submit a\n[pull request](https://github.com/roboflow/multimodal-maestro/pulls).\n",
"bugtrack_url": null,
"license": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n \n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n \n 1. Definitions.\n \n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n \n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n \n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n \n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n \n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n \n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n \n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n \n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n \n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. 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If You\n institute patent litigation against any entity (including a\n cross-claim or counterclaim in a lawsuit) alleging that the Work\n or a Contribution incorporated within the Work constitutes direct\n or contributory patent infringement, then any patent licenses\n granted to You under this License for that Work shall terminate\n as of the date such litigation is filed.\n \n 4. Redistribution. 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In no event and under no legal theory,\n whether in tort (including negligence), contract, or otherwise,\n unless required by applicable law (such as deliberate and grossly\n negligent acts) or agreed to in writing, shall any Contributor be\n liable to You for damages, including any direct, indirect, special,\n incidental, or consequential damages of any character arising as a\n result of this License or out of the use or inability to use the\n Work (including but not limited to damages for loss of goodwill,\n work stoppage, computer failure or malfunction, or any and all\n other commercial damages or losses), even if such Contributor\n has been advised of the possibility of such damages.\n \n 9. Accepting Warranty or Additional Liability. While redistributing\n the Work or Derivative Works thereof, You may choose to offer,\n and charge a fee for, acceptance of support, warranty, indemnity,\n or other liability obligations and/or rights consistent with this\n License. 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"summary": "Streamline the fine-tuning process for vision-language models like PaliGemma 2, Florence-2, and Qwen2.5-VL.",
"version": "1.0.0",
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"Homepage": "https://roboflow.github.io/multimodal-maestro/",
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