haystack
burn
Our great sponsors
| haystack | burn | |
|---|---|---|
| 54 | 34 | |
| 13,633 | 4,845 | |
| 5.8% | - | |
| 9.9 | 8.9 | |
| 3 days ago | 5 months ago | |
| Python | Rust | |
| 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.
haystack
-
Release Radar • March 2024 Edition
View on GitHub
-
First 15 Open Source Advent projects
4. Haystack by Deepset | Github | tutorial
-
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:
-
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.
-
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
-
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
-
Langchain Is Pointless
there is an alternative that is production-grade - deepset haystack https://haystack.deepset.ai/
p.s. i am contributor so there could be bias
burn
-
Burn 0.10.0 Released 🔥 (Deep Learning Framework)
Release Note: https://github.com/burn-rs/burn/releases/tag/v0.10.0
- Deep Learning Framework in Rust: Burn 0.10.0 Released
-
Why Rust Is the Optimal Choice for Deep Learning, and How to Start Your Journey with the Burn Deep Learning Framework
The comprehensive, open-source deep learning framework in Rust, Burn, has recently undergone significant advancements in its latest release, highlighted by the addition of The Burn Book 🔥. There has never been a better moment to embark on your deep learning journey with Rust, as this book will guide you through your initial project, providing extensive explanations and links to relevant resources.
-
Candle: Torch Replacement in Rust
Burn (deep learning framework in rust) has WGPU backend (WebGPU) already. Check it out https://github.com/burn-rs/burn. It was released recently.
- Burn – A Flexible and Comprehensive Deep Learning Framework in Rust
-
Announcing Burn-Wgpu: New Deep Learning Cross-Platform GPU Backend
For more details about the latest release see the release notes: https://github.com/burn-rs/burn/releases/tag/v0.8.0.
-
Are there any ML crates that would compile to WASM?
Tract is the most well known ML crate in Rust, which I believe can compile to WASM - https://github.com/sonos/tract/. Burn may also be useful - https://github.com/burn-rs/burn.
-
Any working wgpu compute example that would run in a browser?
We, the burn team, are working on the wgpu backend (WebGPU) for Burn deep learning framework. You can check out the current state: https://github.com/burn-rs/burn/tree/main/burn-wgpu
-
I’ve fallen in love with rust so now what?
Here is the project: https://github.com/burn-rs/burn
-
Is anyone doing Machine Learning in Rust?
Disclaimer, I'm the main author of Burn https://burn-rs.github.io.
What are some alternatives?
langchain - 🦜🔗 Build context-aware reasoning applications
candle - Minimalist ML framework for Rust
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
dfdx - Deep learning in Rust, with shape checked tensors and neural networks
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
tch-rs - Rust bindings for the C++ api of PyTorch.
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!
Graphite - 2D raster & vector editor that melds traditional layers & tools with a modern node-based, non-destructive, procedural workflow.
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
tract - Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference [Moved to: https://github.com/sonos/tract]
jina - ☁️ Build multimodal AI applications with cloud-native stack
L2 - l2 is a fast, Pytorch-style Tensor+Autograd library written in Rust