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GGML – AI at the Edge

(ggml.ai)
899 points georgehill | 1 comments | 06 Jun 23 16:50 UTC | HN request time: 0.214s | source
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samwillis ◴[06 Jun 23 17:28 UTC] 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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SparkyMcUnicorn ◴[06 Jun 23 19:07 UTC] No.36217604[source]▶
Maybe I'm wrong, but I don't think you want it fine-tuned on your data.

Pretty sure you might be looking for this: https://github.com/SamurAIGPT/privateGPT

Fine-tuning is good for treating it how to act, but not great for reciting/recalling data.

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dr_dshiv ◴[06 Jun 23 21:24 UTC] No.36219307[source]▶
How does this work?
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1. SparkyMcUnicorn ◴[06 Jun 23 23:29 UTC] No.36220553[source]▶
deet already gave a comprehensive answer, but I'll add that the guts of privateGPT are pretty readable and only ~200 lines of code.

Core pieces: GPT4All (LLM interface/bindings), Chroma (vector store), HuggingFaceEmbeddings (for embeddings), and Langchain to tie everything together.

https://github.com/SamurAIGPT/privateGPT/blob/main/server/pr...