falkon
haystack
| falkon | haystack | |
|---|---|---|
| 3 | 55 | |
| 173 | 13,711 | |
| 1.7% | 2.5% | |
| 8.0 | 9.9 | |
| 12 days ago | about 16 hours ago | |
| Python | Python | |
| MIT License | 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.
falkon
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[Research] Optimizing a kernel matrix
As a satisfied customer (thanks!), was about to recommend KeOps as well. It might also be worth looking into falkon which builds on KeOps and leverages Nystrom approximation and conjugate gradient optimisation to further scale kernel operations.
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[D] Have we abandoned kernels?
On the computational side, it is also important to note that kernel methods are now 100-1,000 faster than they were just three years ago. You may be interested by the KeOps library, which is to kernels and geometric ML what cuDNN is to convolutions. You could also have a look at GPyTorch and the Falkon solvers: the software bottlenecks that were holding back kernel methods are progressively being lifted. Million-scale datasets are now routinely processed in minutes/hours and billion-scale problems are starting to become tractable.
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[D] why did kernel methods become less popular than neural networks?
On this note, you may be interested by the KeOps library (which is to kernels/geometric ML what cuDNN is to CNNs) and the Falkon solvers: the software bottlenecks that were holding back kernel methods are progressively being lifted. Million-scale datasets are now routinely processed in minutes/hours and billion-scale problems are starting to become tractable. This opens up quite a few possibilities :-)
haystack
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Haystack DB – 10x faster than FAISS with binary embeddings by default
I was confused for a bit but there is no relation to https://haystack.deepset.ai/
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Release Radar • March 2024 Edition
View on GitHub
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First 15 Open Source Advent projects
4. Haystack by Deepset | Github | tutorial
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Generative AI Frameworks and Tools Every Developer Should Know!
Haystack can be classified as an end-to-end framework for building applications powered by various NLP technologies, including but not limited to generative AI. While it doesn't directly focus on building generative models from scratch, it provides a robust platform for:
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Best way to programmatically extract data from a set of .pdf files?
But if you want an API that you can use to develop your own flow, Haystack from Deepset could be worth a look.
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Which LLM framework(s) do you use in production and why?
Haystack for production. We cannot afford breaking changes in our production apps. Its stable, documentation is excellent and did I mention its' STABLE!??
- Overview: AI Assembly Architectures
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Llama2 and Haystack on Colab
I recently conducted some experiments with Llama2 and Haystack (https://github.com/deepset-ai/haystack), the NLP/LLM framework.
The notebook can be helpful for those trying to load Llama2 on Colab.
1) Installed Transformers from the main branch (and other libraries)
- Build with LLMs for production with Haystack – has 10k stars on GitHub
- Show HN: Haystack – Production-Ready LLM Framework
What are some alternatives?
keops - KErnel OPerationS, on CPUs and GPUs, with autodiff and without memory overflows
langchain - 🦜🔗 Build context-aware reasoning applications
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
jina - ☁️ Build multimodal AI applications with cloud-native stack
BERT-pytorch - Google AI 2018 BERT pytorch implementation
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
gpt-neox - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.