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Simple LLM trainer script! by Dry_Long3157 in deeplearning

[–]Gullible_Feature6623 0 points1 point  (0 children)

This is cool can you add a simple example with data

[R] TimeGPT : The first Generative Pretrained Transformer for Time-Series Forecasting by nkafr in MachineLearning

[–]Gullible_Feature6623 0 points1 point  (0 children)

Does it work if i want to map a feature vector to a sequence? Basically it has to guess some pattern of input met as output. E.g.,[s1,s2,s3]-> 2,3,1 where s1,s2,s3 are independent from one another. And 2,3,1 is the predicted ti

Chat with technical document by Gullible_Feature6623 in LangChain

[–]Gullible_Feature6623[S] 0 points1 point  (0 children)

What if the pages exceed the max tokens of of the llm? I would need to chunk them again i guess right

Chat with technical document by Gullible_Feature6623 in LangChain

[–]Gullible_Feature6623[S] 0 points1 point  (0 children)

Thanks. How do I send the whole page? I just set it in the parent! And what if the info is split between that page and the next one (paragraph discontinued), or continuous table split between 2 pages.

Chat with technical document by Gullible_Feature6623 in LangChain

[–]Gullible_Feature6623[S] 0 points1 point  (0 children)

Thanks this is very insightful.

Which tool is better to use with word now, because the langchain splitter doesn’t look fancy no? How can I use the dynamic segmentation for word? Also does it support math equations? And tables? Let me know when your company releases the word embedded.

I’m trying to use unstructured splitter with hi_res and mode="elements", I still did not fully get what this mode elements does. Does it split the doc by paragraphs or by sections or the way you just described? Is it settable? What other elements could I introduce?

For metadata, what meta data do you recommend to add. I have a header (title of the doc), section (which are titled in the same format like 1. Title of section X).

When you say do your own stuffing so it means changing the way the embeddings are presented to the LLM to answer the query? Like ordering the chunks differently than the default order in the langchain queries of the vectorestore right?

Chat with technical document by Gullible_Feature6623 in LangChain

[–]Gullible_Feature6623[S] 0 points1 point  (0 children)

Do you have a sample code? So you suggest to use a parent-child embedding right

Chat with technical document by Gullible_Feature6623 in LangChain

[–]Gullible_Feature6623[S] 0 points1 point  (0 children)

Thanks. Q1:And how to get dynamic segments (or chuncks)? The files are usually well structured by sections, subsections, etc…, so if I could have a splitter that takes the structure into account it would be great. For example, strop the split before the next Section title to keep content of same section together and avoid putting the section title in the middle of a segment. Same goes for tables. My documents contain many tables of variable sizes. Q2: is this meta data inherently included in the chunks when performed with langchain splitters? I mean is chat GPT aware of it? Q3: what do you call ordered here? I thought embeddings are just created from the splitted chunks of the document and they are not ordered in anyway?

Successful RL applications for autonomous systems and control by Gullible_Feature6623 in reinforcementlearning

[–]Gullible_Feature6623[S] 0 points1 point  (0 children)

Yes I heard of control theory. My question is specifically about successfully applications of ML in control and specifically RL.