pplonski86

Joined 10 years ago

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7,520
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1,903
Member since
Mar 9
New generation of data science tools

https://mljar.com

https://github.com/mljar/mercury

https://github.com/mljar/mljar-supervised

https://runmercury.com

meet.hn/city/pl-Łapy

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Comment · 1 month ago
Comment · 2 months ago
I was able to fit only small models Qwen3.5-4B in RTX3070 which is not very useful for Python and SQL generation thought. When I wan to test larger open LLM models I often just use cloud resources.
Comment · 2 months ago
Yesterday I installed llama.cpp to test it with local AI Data Analyst that I'm building. I was also testing other open LLM providers: Ollama, Jan, vLLM, LM Studio. I had older NVIDIA card (RTX 3070) and llama.cpp instalation was smooth, contrary to vLLM which required me to reinstall CUDA drivers because by default it installed the latest one. I'm curious if there is a speed difference between the same open LLM model served with different runners.

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