nimCSO VS llm_client

Compare nimCSO vs llm_client and see what are their differences.

nimCSO

nim Composition Space Optimization is a high-performance tool leveraging metaprogramming to implement several methods for selecting components (data dimensions) in compositional datasets, as to optimize the data availability and density for applications such as machine learning. (by amkrajewski)

llm_client

Rust library for integrating local LLMs (with llama.cpp) and external LLM APIs. (by ShelbyJenkins)
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nimCSO llm_client
1 1
21 20
- -
9.6 6.6
about 2 months ago about 1 month ago
Nim Rust
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

nimCSO

Posts with mentions or reviews of nimCSO. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-12.
  • Go, Python, Rust, and production AI applications
    4 projects | news.ycombinator.com | 12 Mar 2024
    Nim has also a powerful deep learning library called Arraymancer. It's selling point is that you don't have to rewrite your code from research to production. It's used in various machine learning projects, but one recent one that caught my eye was https://github.com/amkrajewski/nimCSO "Composition Space Optimization"

    https://github.com/mratsim/Arraymancer

llm_client

Posts with mentions or reviews of llm_client. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-12.
  • Go, Python, Rust, and production AI applications
    4 projects | news.ycombinator.com | 12 Mar 2024
    I switched from python to rust for my AI stuff. Honestly, I don't care about the things people say rust is used for. I like it because the package manager, testing, and typings being built into the ecosystem by default makes it so easy to build. VS Python where it all can be done, but you need to then maintain all of those separate tools. The overhead of writing Rust is less than the overhead of dealing with the Python ecosystem. And then you have all the benefits of Rust everyone mentions more often... one other thing no one mentions is the feedback loop between a strongly typed language and copilots ability to more accurately generate code.

    That being said, there is a real shortage of Rust software for Rust only projects. I ended up writing a wrapper for Llama.cpp and open ai API [0] because I needed it and couldn't find anything out there. Eventually, I do intend to implement Hugging Face's Candle library [1] (A rust version of Torch). There is something appealing about doing everything in a single lang especially as the monopoly of CUDA inevitably gets chipped away.

    [0] https://github.com/ShelbyJenkins/llm_client