llimo
dasher-web
llimo | dasher-web | |
---|---|---|
3 | 1 | |
10 | 42 | |
- | - | |
7.6 | 5.8 | |
24 days ago | about 2 months ago | |
JavaScript | JavaScript | |
- | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
llimo
-
Show HN: Next-token prediction in JavaScript – build fast LLMs from scratch
This system predicts "was" as the next word because it usually is the next word after "dog" (in the source data). This library was built to ultimately provide completions, not have a conversation, so no doubt OpenAI's approach works better for chat.
I am however already making a chat model. Here's my approach if anyone cares: The completer already gives great completions and fast, but some of them make no sense to what was asked. The chat model I'm working on here (https://github.com/bennyschmidt/llimo/pull/1) can just get all completions and use parts-of-speech codes to match a completion to the cursor. I don't have this fully implemented yet, but you can get the idea in this PR. This is like an NLP layer specific to chat - has nothing to do with the next-token prediction in general, and there are no NLP libraries in `next-token-prediction` (the npm). The example I've been using to explain this is:
User: "Where is Paris?"
dasher-web
-
Show HN: Next-token prediction in JavaScript – build fast LLMs from scratch
Their library was actually made for dasher.. http://www.inference.org.uk/dasher/ - there was a web version being made (https://github.com/dasher-project/dasher-web We hit a bottleneck with the graphics driving. Note in dasher pretty much the entire tree is in dynamic view). Now this may help to understand the use case. Dasher is for people with disabilities who cant speak. It needs to be a personalised LM that trains on the fly and and keeps track of new words/sentences. But in truth too, utterances are usually small.
Don't get too knocked back by comments. A) If it works - it works. B) Your learning is as valuable as the outcome.
Oh have a look at https://imagineville.org/software/ for some other things that may be of interest..