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899 points georgehill | 2 comments | | HN request time: 0.555s | source
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samwillis ◴[] No.36216196[source]
ggml and llama.cpp are such a good platform for local LLMs, having some financial backing to support development is brilliant. We should be concentrating as much as possible to do local inference (and training) based on privet data.

I want a local ChatGPT fine tuned on my personal data running on my own device, not in the cloud. Ideally open source too, llama.cpp is looking like the best bet to achieve that!

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behnamoh ◴[] No.36216508[source]
I wonder if ClosedAI and other companies use the findings of the open source community in their products. For example, do they use QLORA to reduce the costs of training and inference? Do they quantize their models to serve non-subscribing consumers?
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danielbln ◴[] No.36216688[source]
Not disagreeing with your points, but saying "ClosedAI" is about as clever as writing M$ for Microsoft back in the day, which is to say not very.
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1. loa_in_ ◴[] No.36216958[source]
I'd say saying M$ makes it harder for M$ to find out I'm talking about them in them in the indexed web because it's more ambiguous, that's all I need to know.
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2. coolspot ◴[] No.36218186[source]
If we are talking about indexing, writing M$ is easier to find in an index because it is a such unique token. MS can mean many things (e.g. Miss), M$ is less ambiguous.