this would be the number one alternative @farcaster client today
this would be the number one @farcaster client today — everyone should take notes lol
congratulations to last week’s winners! 🥇 @serendipity 🥈 @ketan 🥉 @campster 🎊🎉🥳
or maybe we can stick the analyzer in https://operator.io’s langchain flow!
… but well probably need to have a django server to be able to use this in product 😕 @limone.eth let me know if you want to collab
👋 im working on this with https://github.com/michaelhly/FarGlot we’ll tune a pretrained BERT model based on the farcaster corpus and expose a sentiment analyzer based on the tuned model should have something out next week!
ha. do you validate the hashes? what’s the hallucination rate?
how do you know if your prompting ability improved? Is it actually like https://warpcast.com/pixel/0xcdf80f
part 2 of the series on backpropagation, hyperparameters and evaluation metrics. writing this gave me some basic understanding of how a LLM works, instead of thinking of a language model as a complete black box. let me know if it helps you too! https://michaelhly.com/posts/tune-llm-two
for example: pretrained model was trained on X corpus, but my data set has some variance, and i want tune the model to adjust for the variance
yes. so this is the hardest part — defining the output/results you want the model to tune towards yes for generative use cases, it's hard to beat openai. i believe people usually tune for niche/task-specific use cases.
a). just exploration b). i'm not sure what you mean by limit. but hugging face has pretrained llamas that you can grab off the shelf: https://huggingface.co/meta-llama my belief is that you should tune a model to find a fit for your dataset ... otherwise you probably don't need to ...
oops. if anyone else hits the 404 https://michaelhly.com/posts/tune-llm-one
for the prompt engineering crowd, i wonder how they're measuring the quality of their prompts ...
i'm figuring out how to train a LLM and documenting each step in this blog series. for anyone who is also curious, here is part 1: https://michaelhly.com/posts/train-llm-one
im learning too! writing a blog series documenting everything along the way: https://michaelhly.com/posts/train-llm-one
i described the issue i ran into here: https://github.com/farcasterxyz/hub-monorepo/issues/1250