domino-research
transformers
| domino-research | transformers | |
|---|---|---|
| 3 | 176 | |
| 76 | 125,369 | |
| - | 1.4% | |
| 0.0 | 10.0 | |
| about 2 years ago | about 8 hours ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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.
domino-research
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[N] Open Sourcing Checkpoint 🛂
Here is a direct URL: https://github.com/dominodatalab/domino-research/tree/main/checkpoint
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Open-sourcing Bridge 🎉
The Domino R&D team is open-sourcing Bridge, a tool that turns your model registry into the declarative source-of-truth for model deployment and hosting.
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[Discussion] Look for service to upload a model and receive a REST API endpoint, for serving predictions
(Disclosure, I am a maintainer on this project) You should checkout Bridge - it deploys models directly from an MLflow registry to SageMaker inference endpoints (hosted APIs). It basically turns your registry into a declarative source of truth for your hosting. The advantage of this approach is that it provides a clean way to update/upgrade your APIs from the same place you're tracking your new versions, experiments etc. One source of truth. You can get an MLflow registry up in a couple minutes if you don't have one.
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?
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
llama - Inference code for Llama models
huggingface_hub - The official Python client for the Huggingface Hub.
transformer-pytorch - Transformer: PyTorch Implementation of "Attention Is All You Need"
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
OpenNMT-py - Open Source Neural Machine Translation and (Large) Language Models in PyTorch