preplish
transformers
preplish | transformers | |
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9 | 176 | |
4 | 125,369 | |
- | 1.7% | |
5.0 | 10.0 | |
7 months ago | 1 day ago | |
Perl | Python | |
GNU General Public License v3.0 only | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
preplish
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Interactive GCC (igcc) is a read-eval-print loop (REPL) for C/C++
> what's wrong with that?
Why nothing at all, of course. A REPL need not be more than a way to test and explore syntax, functions, and logical structures.
> the user experience is REPL-ish and it can help some people learn the _basics_ of the language
PREPLISH exists for Perl ^_^
https://github.com/viviparous/preplish
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online Perl editor
If this is for testing of syntax or of trivial code, it sounds like a good use-case for running a local REPL. (Example: https://github.com/viviparous/preplish)
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Not Your Grandfather’s Perl
This is a simple REPL project and the readme lists other Perl REPLs.
https://github.com/viviparous/preplish
Perl's concise syntax makes working in a REPL a pleasure. Python has a REPL but the design of the language makes it expand both in length (for loops) and in width (tabs).
I am a recent convert to working in a REPL first to test programming ideas.
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Has someone curated Perl data science resources somewhere? I've seen many such collections for other languages. Something like this, but with more modules and what they do:
I made this solution for some of my simple data wrangling: https://github.com/viviparous/preplish
- Is there any good reason not to use perl scripts in place of bash logic?
- Working with __DATA__ sections without Mojolicious
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Acme-ConspiracyTheory-Random
I tried the module it in a Perl REPL (https://github.com/viviparous/preplish) and got the following ravings that are worthy of a US loony politician:
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On Repl-Driven Programming
I agree with you that the immediate start-up and feedback is a great benefit to the coder. This is why I dislike complex, Rube-Goldbergian REPL systems.
There is a use-case for a throw-away interaction with a REPL. For example, how does $builtinFuncX work, or how would $data best be imported into a structure?
A REPL can also be a good initial approach to a more ambitious problem. In this case, a REPL can be good for focus and discipline.
If the second case is going to answer your concern and be constructive, it's necessary to be able to build the code for sharing and cleanly export the code for re-use.
I've had success tackling challenges using REPLs for Python and Perl [1] in both ways. But no tooling is going to solve the problem of a sloppy teammate who claims success just because "it compiles" and "it works on my box". A person who knows how to build good tooling goes further.
[1] https://github.com/viviparous/preplish
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Interactive C++ for Data Science
It is Jupyter is a Rube-Goldbergian nightmare. Python is a memory hog. There are better solutions, to be sure.
A simple REPL is all that's needed to both do A-type and B-type data exploration. (I won't use the term "data scientist", it's an exaggeration in most cases.)
Python has a REPL, R has a REPL, Perl has PDL and both a simple REPL (https://github.com/viviparous/preplish) and a more complex one (https://metacpan.org/pod/Reply).
Jupyter should not be used as an IDE because it is the wrong tool for development. A-type data explorers just want a painless UI and may not care much about the horrible agglutination of incomplete/slow/broken solutions that Jupyter represents.
transformers
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AI enthusiasm #9 - A multilingual chatbot📣🈸
transformers is a package by Hugging Face, that helps you interact with models on HF Hub (GitHub)
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Maxtext: A simple, performant and scalable Jax LLM
Is t5x an encoder/decoder architecture?
Some more general options.
The Flax ecosystem
https://github.com/google/flax?tab=readme-ov-file
or dm-haiku
https://github.com/google-deepmind/dm-haiku
were some of the best developed communities in the Jax AI field
Perhaps the “trax” repo? https://github.com/google/trax
Some HF examples https://github.com/huggingface/transformers/tree/main/exampl...
Sadly it seems much of the work is proprietary these days, but one example could be Grok-1, if you customize the details. https://github.com/xai-org/grok-1/blob/main/run.py
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Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
The HuggingFace transformers library already has support for a similar method called prompt lookup decoding that uses the existing context to generate an ngram model: https://github.com/huggingface/transformers/issues/27722
I don't think it would be that hard to switch it out for a pretrained ngram model.
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AI enthusiasm #6 - Finetune any LLM you want💡
Most of this tutorial is based on Hugging Face course about Transformers and on Niels Rogge's Transformers tutorials: make sure to check their work and give them a star on GitHub, if you please ❤️
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Schedule-Free Learning – A New Way to Train
* Superconvergence + LR range finder + Fast AI's Ranger21 optimizer was the goto optimizer for CNNs, and worked fabulously well, but on transformers, the learning rate range finder sadi 1e-3 was the best, whilst 1e-5 was better. However, the 1 cycle learning rate stuck. https://github.com/huggingface/transformers/issues/16013
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Gemma doesn't suck anymore – 8 bug fixes
Thanks! :) I'm pushing them into transformers, pytorch-gemma and collabing with the Gemma team to resolve all the issues :)
The RoPE fix should already be in transformers 4.38.2: https://github.com/huggingface/transformers/pull/29285
My main PR for transformers which fixes most of the issues (some still left): https://github.com/huggingface/transformers/pull/29402
- HuggingFace Transformers: Qwen2
- HuggingFace Transformers Release v4.36: Mixtral, Llava/BakLlava, SeamlessM4T v2
- HuggingFace: Support for the Mixtral Moe
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Paris-Based Startup and OpenAI Competitor Mistral AI Valued at $2B
If you want to tinker with the architecture Hugging Face has a FOSS implementation in transformers: https://github.com/huggingface/transformers/blob/main/src/tr...
If you want to reproduce the training pipeline, you couldn't do that even if you wanted to because you don't have access to thousands of A100s.
What are some alternatives?
xeus-cling - Jupyter kernel for the C++ programming language
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
tinyspec-cling - tiny spectral synthesizer with livecoding support
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
examples - Fully-working mlpack example programs
llama - Inference code for Llama models
jupyter - An interface to communicate with Jupyter kernels.
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
slimux - SLIME inspired tmux integration plugin for Vim
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
vim-slime - A vim plugin to give you some slime. (Emacs)
huggingface_hub - The official Python client for the Huggingface Hub.