LocalLLaMA
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
Get support from the community! Ask questions, share prompts, discuss benchmarks, get hyped at the latest and greatest model releases! Enjoy talking about our awesome hobby.
As ambassadors of the self-hosting machine learning community, we strive to support each other and share our enthusiasm in a positive constructive way.
Rules:
Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.
Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.
Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.
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That's the impression I got from playing with it. I don't really use LLMs for anything practical, so I haven't done anything too serious with it. Here's are a couple examples of having it write fiction: https://gist.github.com/KerfuffleV2/4ead8be7204c4b0911c3f3183e8a320c
I also tried with plain old llama-65B: https://gist.github.com/KerfuffleV2/46689e097d8b8a6b3a5d6ffc39ce7acd
You can see it makes some weird mistakes (although the writing style itself is quite good).
If you want to give me a prompt, I can feed it to guanaco-65B and show you the result.
These are, indeed, pretty good, and quite coherent.
I was pretty impressed by guanaco-65B, especially how it was able to remain coherent even way past the context limit (with llama.cpp's context wrapping thing). You can see the second story is definitely longer than 2,048 tokens.