Package Health Score

78 / 100

security
No known security issues
popularity
Small
maintenance
Healthy
community
Sustainable

Popularity

Small

GitHub Stars: 4.39K
Forks: 471
Contributors: 50

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package fms-acceleration-peft, we found that it has been starred 4,388 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Oct 25, 2024
Direct Vulnerabilities: 0
C
0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

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Maintenance

Healthy

Commit Frequency

Open Issues: 220
Open PR: 30
Last Release: 7 days ago
Last Commit: 1 month ago

Further analysis of the maintenance status of fms-acceleration-peft based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Healthy.

We found that fms-acceleration-peft demonstrates a positive version release cadence with at least one new version released in the past 3 months.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Sustainable

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 50
Funding: No

With more than 10 contributors for the fms-acceleration-peft repository, this is possibly a sign for a growing and inviting community.

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Package

Python Versions Compatibility: ~=3.9

Age: 4 months
Latest Release: 7 days ago
Dependencies: 0 Direct
Versions: 10
Maintainers: 2
Wheels: OS Independent

Readme

FMS Acceleration for Accelerated PeFT Techniques

Currently only supports LoRA-related techniques, but more are in the pipeline to be added:

Plugins

Plugin	Description	Depends	Loading	Augmentation	Callbacks
autogptq	Loads 4bit GPTQ-LoRA with quantized GPTQ as base	AutoGPTQ	✅	✅	✅
bnb	Loads 4bit QLoRA with quantized bitsandbytes Linear4	Huggingface bitsandbytes	✅	✅	✅

Key Points

fix upcasting (resulting in slowdown) issue for bnb plugin, originally discovered by inventors of Unsloth. NOTE: we recommend using mixed precision when using 4bit quant for better performance, as per our benchmarks.
bnb properly configured to work with FSDP following this guide.
triton_v2 kernels are not yet properly integrated into huggingface optimum.
triton_v2 kernels are the only 4bit kernels that work for training.

GPTQ-LORA's AutoGPTQ - Current Implementation vs Legacy Implementation

GPTQ-LORA depends on an AutoGPTQ backend to run. There are 2 backend options

Current Implementation
- This is an extracted local subset from ModelCloud's refactored fork.
- It removes redundant code to simplify build and installation of the plugin
Legacy Implementation
- This requires building the package from the official AutoGPTQ repository
- To replicate this implementation, follow the installation below
  - The legacy implementation of GPTQ-LORA uses an external AutoGPTQ package, you must ensure the specific commit is installed
```
pip install git+https://github.com/AutoGPTQ/AutoGPTQ.git@ea829c7bbe83561c2b1de26795b6592992373ef7
```
  - To construct the plugin, in the configuration object that is passed to the plugin - set use_external_lib: True (otherwise defaults to use the local AutoGPTQ package)
```
    peft:
    quantization: 
        auto_gptq:
        kernel: triton_v2
        from_quantized: True
        use_external_lib: True
```

Known Issues

GPTQ-LORA sometimes observed to have nan grad norms in the begining of training, but training proceeds well otherwise.

FAQs

What is fms-acceleration-peft?

FMS Acceleration for PeFT. Visit Snyk Advisor to see a full health score report for fms-acceleration-peft, including popularity, security, maintenance & community analysis.

Is fms-acceleration-peft popular?

We’re still processing downloads data for the python package fms-acceleration-peft. Visit the popularity section on Snyk Advisor to see the full popularity analysis.

Is fms-acceleration-peft well maintained?

We found that fms-acceleration-peft demonstrated a healthy version release cadence and project activity. It has a community of 50 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is fms-acceleration-peft safe to use?

The python package fms-acceleration-peft was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 28 October-2024, at 23:39 (UTC).

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fms-acceleration-peft

FMS Acceleration for PeFT For more information about how to use this package see README

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Package Health Score

Popularity

Direct Usage Popularity

Security

Security and license risk for latest version

Maintenance

Commit Frequency

Community

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Package

Readme

FMS Acceleration for Accelerated PeFT Techniques

Plugins

Key Points

GPTQ-LORA's AutoGPTQ - Current Implementation vs Legacy Implementation

Known Issues

FAQs

What is fms-acceleration-peft?

Is fms-acceleration-peft popular?

Is fms-acceleration-peft well maintained?

Is fms-acceleration-peft safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Fix identified vulnerabilities

Monitor for new issues

Package Health Score

Direct Usage Popularity

Security and license risk for latest version

Commit Frequency

Embed Package Health Score Badge

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Check your requirements.txt

Snyk Vulnerability Scanner

Readme

FMS Acceleration for Accelerated PeFT Techniques

Plugins

Key Points

GPTQ-LORA's AutoGPTQ - Current Implementation vs Legacy Implementation

Known Issues

FAQs

What is fms-acceleration-peft?

Is fms-acceleration-peft popular?

Is fms-acceleration-peft well maintained?

Is fms-acceleration-peft safe to use?

Build a secure application checklist

Select a recommended open source package

Scan your app for vulnerabilities

Source Code

Open Source

Container

Config

Fix identified vulnerabilities

Monitor for new issues