Mistral 8x7B 32k model [magnet]

• megablocks-public

  5 857 9.0
  

https://github.com/mistralai/megablocks-public

Oddly absent: an over-rehearsed professional release video talking about a revolution in AI.

If people are wondering why there is so much AI activity right around now, it's because the biggest deep learning conference (NeurIPS) is next week.

https://twitter.com/karpathy/status/1733181701361451130

• mistral-src

  9 8,666 7.3 Jupyter Notebook

  Reference implementation of Mistral AI 7B v0.1 model.

  

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• bliss

  3 9 6.1 Python

  🧘 BLISS – a Benchmark for Language Induction from Small Sets (by taucompling)

I wonder how it will rank on benchmarks which are password-protected to prevent test contamination, for example:

https://github.com/taucompling/bliss

• llama-mistral

  5 374 8.4 Python

  Inference code for Mistral and Mixtral hacked up into original Llama implementation

If anyone can help running this, would be appreciated. Resources so far:

- https://github.com/dzhulgakov/llama-mistral

• slint

  138 15,103 9.9 Rust

  Slint is a declarative GUI toolkit to build native user interfaces for Rust, C++, or JavaScript apps.

  

I didn't know about RAG, thanks for sharing. I am not sure, if outdated information can be tackled with RAG though, especially in coding.

Just today, i asked GPT and Bard(Gemini) to write code using slint, neither of them had any idea of slint. Slint being a relatively new library, like two and a half (0.1 version) to one and a half (0.2 version) years back [1] is not something they trained on.

Natural language doesn't change that much over the course of a handful of years, but in coding 2 years back may as well be a century. My argument is that, SmallLMs not only they are relevant, they are actually desirable, if the best solution is to be retrained from scratch.

If on the other hand a billion token context window proves to be practical, or the RAG technique solves most of use cases, then LLMs might suffice. This RAG technique, could it be aware, of million of git commits daily, on several projects, and keep it's knowledge base up to date? I don't know about that.

https://github.com/slint-ui/slint/releases?page=3

• SaaSHub

  www.saashub.com featured

  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

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