Bumblebee Alternatives

bumblebee reviews and mentions

• Implementing Natural Conversational Agents with Elixir
  4 projects | news.ycombinator.com | 18 Apr 2024

  Despite some limitations, you will probably find Bumblebee (https://github.com/elixir-nx/bumblebee) interesting.

  "Bumblebee provides pre-trained Neural Network models on top of Axon. It includes integration with HuggingFace Models, allowing anyone to download and perform Machine Learning tasks with few lines of code"

• Running Open-Source AI Models Locally with Ruby
  2 projects | news.ycombinator.com | 5 Feb 2024

  That's not bad at all!

  There is also Nx and Bumblebee in Elixir land - it really changes the how one approaches running models in production. The fact that one can put together a service (or local process) running any model published to hugging face in a couple of lines of code is amazing.

  [0] https://github.com/elixir-nx/bumblebee/blob/main/examples/ph...

  [1] https://gist.github.com/toranb/8be408eaa97d5a5b795aec7d7fbee...

• An example of semantic search with Elixir and Bumblebee
  1 project | dev.to | 13 Dec 2023

  Theses notes describes how this can be done with the Elixir language and Nx, Axon (Nx-powered Neural Network library) and Bumblebee which provides pre-trained Neural Network models.

• Elixir Livebook is a secret weapon for documentation
  12 projects | news.ycombinator.com | 6 Aug 2023

  Apart from running code inside a "markdown" file, livebook can do much more. You have Smart cells to show charts, run sql queries against a db, run Neural Network tasks such as Image-To-Text generation using Bumblebee[1], etc. It is collaborative as well.

  [1] https://github.com/elixir-nx/bumblebee

• “Machine Learning in Elixir” (Beta Book)
  1 project | news.ycombinator.com | 19 Jul 2023

  Elixir is an increasingly interesting platform for ML (see Nx, Axon, and more recently BumbleBee https://github.com/elixir-nx/bumblebee). I'm pretty happy to see this book released in beta.

• Data wrangling in Elixir with Explorer, the power of Rust, the elegance of R
  7 projects | news.ycombinator.com | 14 Apr 2023

  José from the Livebook team. I don't think I can make a pitch because I have limited Python/R experience to use as reference.

  My suggestion is for you to give it a try for a day or two and see what you think. I am pretty sure you will find weak spots and I would be very happy to hear any feedback you may have. You can find my email on my GitHub profile (same username).

  In general we have grown a lot since the Numerical Elixir effort started two years ago. Here are the main building blocks:

  * Nx (https://github.com/elixir-nx/nx/tree/main/nx#readme): equivalent to Numpy, deeply inspired by JAX. Runs on both CPU and GPU via Google XLA (also used by JAX/Tensorflow) and supports tensor serving out of the box

  * Axon (https://github.com/elixir-nx/axon): Nx-powered neural networks

  * Bumblebee (https://github.com/elixir-nx/bumblebee): Equivalent to HuggingFace Transformers. We have implemented several models and that's what powers the Machine Learning integration in Livebook (see the announcement for more info: https://news.livebook.dev/announcing-bumblebee-gpt2-stable-d...)

  * Explorer (https://github.com/elixir-nx/explorer): Series and DataFrames, as per this thread.

  * Scholar (https://github.com/elixir-nx/scholar): Nx-based traditional Machine Learning. This one is the most recent effort of them all. We are treading the same path as scikit-learn but quite early on. However, because we are built on Nx, everything is derivable, GPU-ready, distributable, etc.

  Regarding visualization, we have "smart cells" for VegaLite and MapLibre, similar to how we did "Data Transformations" in the video above. They help you get started with your visualizations and you can jump deep into the code if necessary.

  I hope this helps!

• Distributed² Machine Learning Notebooks with Elixir and Livebook
  7 projects | news.ycombinator.com | 11 Apr 2023

  The current pipeline expects PCM audio and, if data is coming from a microphone in the browser, you can do the initial processing and conversion in the browser (see the JS in this single file Phoenix app speech to text example [0]).

  On the other hand, if you expect a variety of formats (mp3, wav, etc), then shelling out or embedding ffmpeg is probably the quickest path to achieve something. The Membrane Framework[1] is an option here too which includes streaming. I believe Lars is going to do a cool demo with Membrane at ElixirConf EU next week.

  [0]: https://github.com/elixir-nx/bumblebee/blob/main/examples/ph...

  [1]: https://membrane.stream/

• Do I need to use Elixir from Go perspective?
  5 projects | /r/elixir | 9 Jan 2023

  Outside of that, Elixir can be used for data pipelines, audio-video processing, and it is making inroads on Machine Learning with projects like Livebook, Nx, and Bumblebee.

• Riffusion – Stable Diffusion fine-tuned to generate Music
  7 projects | news.ycombinator.com | 15 Dec 2022
• Can bumblebee be used in gleam?
  1 project | /r/gleamlang | 8 Dec 2022
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