3 ms·

Ok, not to belittle your work or anything, but I think you are either not using the words correctly, or trolling us here. If we used your "non-LLM" library to

Ok, not to belittle your work or anything, but I think you are either not using the words correctly, or trolling us here. If we used your "non-LLM" library to build an LLM, it wouldn't be "from scratch" would it? Therefore it is natural to assume that you meant that your code is supposed to be "an LLM written from scratch".

_akhe 3y ago
Not trolling, though I feel the same way about you with this post haha. To answer your question, this library doesn't provide something like a `chat` method and doesn't provide anything like Stable Diffusion's `img2img` API either - that's not what it is - it's a library for predicting the next token (letter, word, pixel, etc.) based on data you train it on. The most typical use cases for this model are: Auto-completion, auto-correct, spell check, search, etc. However, if you watched the incredible video I shared in the original post you'd know that this completion concept can be applied to other interfaces: Chat, image generation, audio analysis/generation, etc. and in fact it is applied to LLMs like GPT. This library doesn't get into any chat-specific methods like `ask` (question & answer), it just completes token sequences. If your goal is to create a ChatGPT clone, you would have to add methods like `chat`, `ask`, etc. and provide code for ongoing conversations - probably using an NLP library to parse queries into cursors like I'm trying in another project, or use "MULTI-HEADED ATTENTION BLOCKS" (lmfao) if you fancy that. Or if you wanted to create a Stable Diffusion clone, you'd have to provide those image-related methods needed. As far as the meaning of "from scratch" - I mean compared to using Ollama etc. to run local models or using OpenAI - just trying to enable you to build whatever model suits your data and use case.

3 ms·

Ok, not to belittle your work or anything, but I think you are either not using the words correctly, or trolling us here. If we used your "non-LLM" library to

by selcuka 3y ago

_akhe 3y ago
Not trolling, though I feel the same way about you with this post haha. To answer your question, this library doesn't provide something like a `chat` method and doesn't provide anything like Stable Diffusion's `img2img` API either - that's not what it is - it's a library for predicting the next token (letter, word, pixel, etc.) based on data you train it on. The most typical use cases for this model are: Auto-completion, auto-correct, spell check, search, etc. However, if you watched the incredible video I shared in the original post you'd know that this completion concept can be applied to other interfaces: Chat, image generation, audio analysis/generation, etc. and in fact it is applied to LLMs like GPT. This library doesn't get into any chat-specific methods like `ask` (question & answer), it just completes token sequences. If your goal is to create a ChatGPT clone, you would have to add methods like `chat`, `ask`, etc. and provide code for ongoing conversations - probably using an NLP library to parse queries into cursors like I'm trying in another project, or use "MULTI-HEADED ATTENTION BLOCKS" (lmfao) if you fancy that. Or if you wanted to create a Stable Diffusion clone, you'd have to provide those image-related methods needed. As far as the meaning of "from scratch" - I mean compared to using Ollama etc. to run local models or using OpenAI - just trying to enable you to build whatever model suits your data and use case.