transformers
xeus-cling
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| transformers | xeus-cling | |
|---|---|---|
| 175 | 15 | |
| 124,557 | 2,945 | |
| 2.7% | 1.8% | |
| 10.0 | 4.6 | |
| 6 days ago | 9 days ago | |
| Python | C++ | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
transformers
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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.
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Fail to reproduce the same evaluation metrics score during inference.
I am aware that using mixed precision reduces the stability of weight and there will be little consistency but don't expect it to be this much. I have attached the graph of evaluation metrics. If someone can give me some insight into this issue, that would be great.
xeus-cling
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Interactive GCC (igcc) is a read-eval-print loop (REPL) for C/C++
More recent activity, but based on clang: https://github.com/jupyter-xeus/xeus-cling https://github.com/root-project/cling
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TermiC: Terminal C, Interactive C/C++ REPL shell created with BASH
If you like interactive c/c++, how a look at https://github.com/jupyter-xeus/xeus-cling, that allow you to run the c/c++ repl in Jupyter, either in web interface, and terminal interfaces.
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IDE for CPP(leetcode)
There are Cpp intepreters like Cling. There are even cpp notebooks like https://github.com/jupyter-xeus/xeus-cling. If that's an "IDE" it's questionable
- How does 3[a] gives the element at index 3 in an array?
- For those defending Python and citing Jupyter notebook scripting as the reason
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Why tho?
Holy shit, its actually a thing for C++ https://github.com/jupyter-xeus/xeus-cling. Now if only there was a C version...
- Changing std:sort at Google’s Scale and Beyond
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Jupyter refuses C++
Links I tried and failed:https://github.com/jupyter-xeus/xeus-cling
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How to write multiple programs in one c file? (like we can do for python files in jupyter notebook )
Are you talking about interpreted C++? Xeus-cling is your friend (i.e., C++ interpreter).
- Turns Jupyter notebooks into standalone web applications and dashboards
What are some alternatives?
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
pybind11 - Seamless operability between C++11 and Python
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
jupyterlite - Wasm powered Jupyter running in the browser 💡
llama - Inference code for Llama models
cling - The cling C++ interpreter
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
examples - Fully-working mlpack example programs
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
Pluto.jl - 🎈 Simple reactive notebooks for Julia
huggingface_hub - The official Python client for the Huggingface Hub.
sanitizers - AddressSanitizer, ThreadSanitizer, MemorySanitizer