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Top 17 Python Alignment Projects
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Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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3DDFA_V2
The official PyTorch implementation of Towards Fast, Accurate and Stable 3D Dense Face Alignment, ECCV 2020.
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aeneas
aeneas is a Python/C library and a set of tools to automagically synchronize audio and text (aka forced alignment)
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gangealing
Official PyTorch Implementation of "GAN-Supervised Dense Visual Alignment" (CVPR 2022 Oral, Best Paper Finalist)
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facexlib
FaceXlib aims at providing ready-to-use face-related functions based on current STOA open-source methods.
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InfluxDB
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mesh_mesh_align_plus
Precisely align, move, and measure+match objects and mesh parts in your 3D scenes.
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HALOs
A library with extensible implementations of DPO, KTO, PPO, and other human-aware loss functions (HALOs).
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rmsd
Calculate Root-mean-square deviation (RMSD) of two molecules, using rotation, in xyz or pdb format
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subaligner
Automatically synchronize and translate subtitles, or create new ones by transcribing, using pre-trained DNNs, Forced Alignments and Transformers. https://subaligner.readthedocs.io/
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ExpertLLaMA
An opensource ChatBot built with ExpertPrompting which achieves 96% of ChatGPT's capability.
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unsupervisedRR
[CVPR 2021 - Oral] UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
Project mention: The GitHub Black Market That Helps Coders Cheat the Popularity Contest | news.ycombinator.com | 2023-10-23> Another giveaway is the ratio of stars to watchers / forks. I remember one project with thousands of stars but only 10 users "watching" it. They went on to raise a sizable seed round too.
Not necessarily indicative of foul play. I have two projects like this (https://github.com/smacke/ffsubsync and https://github.com/ipyflow/ipyflow) and I attribute it to not having great developer documentation.
Project mention: Looking for a NLP expert to help me in a project about body movements and facial expressions. | /r/MLQuestions | 2023-04-27[2] https://github.com/yfeng95/DECA
Project mention: stable diffusion downloads something from github when making a image | /r/StableDiffusion | 2023-07-22"https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth"
I came across this open source library a few times across Reddit + HN and something that piqued my interest was their concept around "test-driven alignment".
If you are using no-code solutions, increasing an "idea" in a dataset will make that idea more likely to appear.
If you are fine-tuning your own LLM, there are other ways to get your idea to appear. In the literature this is sometimes called RLHF or preference optimization, and here are a few approaches:
Direct Preference Optimization
This uses Elo-scores to learn pairwise preferences. Elo is used in chess and basketball to rank individuals who compete in pairs.
@argilla_io on X.com has been doing some work in evaluating DPO.
Here is a decent thread on this: https://x.com/argilla_io/status/1745057571696693689?s=20
Identity Preference Optimization
IPO is research from Google DeepMind. It removes the reliance of Elo scores to address overfitting issues in DPO.
Paper: https://x.com/kylemarieb/status/1728281581306233036?s=20
Kahneman-Tversky Optimization
KTO is an approach that uses mono preference data. For example, it asks if a response is "good or not." This is helpful for a lot of real word situations (e.g. "Is the restaurant well liked?").
Here is a brief discussion on it:
https://x.com/ralphbrooks/status/1744840033872330938?s=20
Here is more on KTO:
* Paper: https://github.com/ContextualAI/HALOs/blob/main/assets/repor...
* Code: https://github.com/ContextualAI/HALOs
Project mention: ExpertPrompting: Instructing Large Language Models to be Distinguished Experts | /r/singularity | 2023-05-25The answering quality of an aligned large language model (LLM) can be drastically improved if treated with proper crafting of prompts. In this paper, we propose ExpertPrompting to elicit the potential of LLMs to answer as distinguished experts. We first utilize In-Context Learning to automatically synthesize detailed and customized descriptions of the expert identity for each specific instruction, and then ask LLMs to provide answer conditioned on such agent background. Based on this augmented prompting strategy, we produce a new set of instruction-following data using GPT-3.5, and train a competitive open-source chat assistant called ExpertLLaMA. We employ GPT4-based evaluation to show that 1) the expert data is of significantly higher quality than vanilla answers, and 2) ExpertLLaMA outperforms existing open-source opponents and achieves 96\% of the original ChatGPT's capability. All data and the ExpertLLaMA model will be made publicly available at this https URL.
The first, called trajectopy, stands as a full-fledged application featuring a PyQt6-based graphical user interface (GUI). This GUI-driven platform simplifies trajectory-related tasks and offers an intuitive user experience. For those desiring a more in-depth approach, there is trajectopy-core. This backend implementation without any PyQt6 dependencies provides essential functionality e.g. for computing absolute trajectory error (ATE) and relative pose error (RPE).
The first, called trajectopy, stands as a full-fledged application featuring a PyQt6-based graphical user interface (GUI). This GUI-driven platform simplifies trajectory-related tasks and offers an intuitive user experience. For those desiring a more in-depth approach, there is trajectopy-core. This backend implementation without any PyQt6 dependencies provides essential functionality e.g. for computing absolute trajectory error (ATE) and relative pose error (RPE).
Python Alignment related posts
- Help Us Build Our Roadmap – Pydantic
- Mastering Pandas read_csv() with Examples - A Tutorial by Codes With Pankaj
- How do people know when to use what programming language?
- stable diffusion downloads something from github when making a image
- Mesh Align Plus 1.0
- Declutter your Gmail inbox with Python: A Step-by-Step Guide
- Which software is suitable for achieving my goal?
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A note from our sponsor - InfluxDB
www.influxdata.com | 24 Apr 2024
Index
What are some of the best open-source Alignment projects in Python? This list will help you:
Project | Stars | |
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1 | Pandas | 41,923 |
2 | ffsubsync | 6,495 |
3 | 3DDFA_V2 | 2,777 |
4 | aeneas | 2,379 |
5 | DECA | 2,005 |
6 | gangealing | 1,008 |
7 | facexlib | 741 |
8 | DataDreamer | 632 |
9 | tanuki.py | 634 |
10 | mesh_mesh_align_plus | 540 |
11 | HALOs | 525 |
12 | rmsd | 463 |
13 | subaligner | 415 |
14 | ExpertLLaMA | 289 |
15 | unsupervisedRR | 134 |
16 | trajectopy | 20 |
17 | trajectopy-core | 1 |
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