Practical overview
When it fits
Use Passllm for authorized, consent-based research or password audits where the goal is to evaluate targeted password guesses generated from relevant personal information.
Who it is for
- Security researchers studying targeted password-guessing methods.
- Authorized password-auditing teams evaluating how personal information affects password candidates.
Documented uses
- Generate ranked password candidates from profile information in a controlled assessment.
- Prepare pretrained model weights and hardware settings for inference.
- Study model fine-tuning with LoRA in password-guessing research.
Requirements & limits
The repository is public and identifies the project as GPL-3.0. It documents self-hosted use and a pretrained-model demo notebook in Google Colab; the README says the project is temporarily on hold.
- The workflow processes sensitive personal information, including examples such as names, birthdays, emails and prior passwords.
- Inference and training have different hardware guidance: pretrained inference includes CPU options, while the training guidance specifies an NVIDIA GPU with CUDA.
- AMD support depends on operating system and backend; the README specifies ROCm and PyTorch for ROCm on Linux or WSL.
- The project reports its own benchmark results; those claims do not establish performance for an individual dataset or assessment.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up Passllm for pretrained inference
Prepare the repository and pretrained weights for a later password-candidate generation run.
Before you start
- Python 3.10 or later.
- A supported CPU or GPU environment; the repository lists CPU inference and NVIDIA and AMD GPU configurations.
- Repository dependencies and the pretrained weights placed in the models/ directory.
Steps
Clone the PassLLM repository and install dependencies from requirements.txt, or use the listed manual package installation.
Download the pretrained weights and place them in the models/ directory.
Adjust the WebUI or src/config.py settings for the available hardware.
Expected outcome
The repository and pretrained model are configured for the separately documented WebUI or CLI inference stage.
Guide limitations
- This guide covers setup only; it does not run inference.
- The README provides different device, precision and batch-size settings by hardware. AMD on Linux or WSL requires ROCm and PyTorch for ROCm.
- Use personal information only for authorized, consented research.
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 |
|---|---|---|---|
| Passllm | Generate ranked password candidates from profile information in a controlled assessment. | Linux, Self-hosted | The repository is public and identifies the project as GPL-3.0. It documents self-hosted use and a pretrained-model demo notebook in Google Colab; the README says the project is temporarily on hold. |
| Pulse Of Motion | Estimate a video's physical frame rate from its visual dynamics. | Linux | The repository is public. Its README describes a released checkpoint that downloads from Hugging Face on first inference, with a manual download option. The strongest internal model is not fully released; the README directs interested users to contact the project about access. No license information is provided in the repository evidence. |
| Neural Image Assessment | Train the model using AVA images and rating annotations. | Cloud, Linux, macOS, Windows |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 12 October 2026
- Original website title
- Tzohar/PassLLM · Official homepage title · retained in its source language