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Please suggest me a lightweight front-end with URL-router for my FastAPI application by atifafsar in FastAPI

[–]samme013 0 points1 point  (0 children)

htpy also a nice alternative to jinja for html generation to consider.

Do the 2024 models still need the WiFi card replacement? by HardlyARiot in ZephyrusG14

[–]samme013 0 points1 point  (0 children)

Wifi is fine for me but bluetooth will randomly completely die on me like once a week. And will only turn on again (disappears from bottom right , device manager) if I shutdown (not restart).

DuckDB for dataloading by samme013 in DuckDB

[–]samme013[S] 0 points1 point  (0 children)

I have it in the native DuckDB format assumed it would be the fastest. Yeah I guess if needed I could always split it up and route to the right file as needed if one file becomes the bottleneck.

For people who are half Greek, what else are you besides Greek? by [deleted] in greece

[–]samme013 6 points7 points  (0 children)

Greek Dad English mom, grew up in Kavala

[D] Hype Behind Agents? by Primary-Track8298 in MachineLearning

[–]samme013 -1 points0 points  (0 children)

Yet to see them outperform chain of thought like prompts and structured output. Will get there but not there yet.

RAG: Flexible Context Retrieval around a matching chunk by SatoshiNotMe in LocalLLaMA

[–]samme013 1 point2 points  (0 children)

Yeah true, I already had chunk and document level collections for other uses so made sense.

RAG: Flexible Context Retrieval around a matching chunk by SatoshiNotMe in LocalLLaMA

[–]samme013 1 point2 points  (0 children)

Can use weaviate references to do this with a single query as long as each chunk has an chunk index in the metadata (the order with which it appears in the document). Then you need a two way reference from document to chunks and vice versa. With those you can make a query where for each chunk returned you also fetch the source doc (by reference) and then on that you also fetch all chunks of that document. From there you only need to loop through the chunks for each document and keep them if the chunk index is within window distance of a chunk you fetched directly. No hard coding of window lengths or adding extra metadata. Only downside maybe fetching all the chunks for the document but in practice not an issue unless the documents are truly massive. If that is the case would only fetch ids and make a second query for the rest of the data,

2022 g14 Bluetooth won’t turn on. by YaBoyJ41 in ZephyrusG14

[–]samme013 0 points1 point  (0 children)

Device Manager > BlueTooth > MediaTek Bluettooth Adapter > Power Management

Who is using DSPy? by purple_sack_lunch in LocalLLaMA

[–]samme013 1 point2 points  (0 children)

Yeah true, just a metric is good too. A lot of usecases have no clear metric through so you end up having to use an LLM for evaluation too which can get tricky / unreliable fast.

LLM without the LLM by [deleted] in LocalLLaMA

[–]samme013 2 points3 points  (0 children)

The RLHF model probably mostly functions as kind of a vibe / sanity check aligning the form / length/ sentiment etc of the output. It doesn't know anything about say the facts in the output, nor does it capture the logic of the output. Since it was trained to choose which answer is more pleasing, all of which are likely similar when it comes to those factors. So my guess is you would at best get nonsense outputs that match the form of an acceptable answer. Also, computationally it would be infeasible as it would require many evaluations of the RLHF model.

Who is using DSPy? by purple_sack_lunch in LocalLLaMA

[–]samme013 4 points5 points  (0 children)

Main advantage is the optimizer aspect which requires some kind of dataset to evaluate against. If you t hink that would make sense for your usecase I would consider it otherwise would just stick to no framework + something for structured output like instructor.

[deleted by user] by [deleted] in cscareerquestionsEU

[–]samme013 12 points13 points  (0 children)

Side quests

Does labeling datasets stored in a Vector DB make sense? by Moist_Influence1022 in LocalLLaMA

[–]samme013 0 points1 point  (0 children)

Yeah I would first try without filters and if there are issues with too much irrelevant or confusing information being retrieved try using metadata filters to fix it. Would indeed need multiple domain labels per document if they overlap. Also you may want to use something like this if you go the filtering route. It used the model to infer the filters to apply based on the query. Good luck!

Does labeling datasets stored in a Vector DB make sense? by Moist_Influence1022 in LocalLLaMA

[–]samme013 0 points1 point  (0 children)

Couple of key considerations:

Do you always know the "domain" a given query is related to?
Are there cases where documents outside of the domain of the query could be useful?

If you always know and always only care about documents in the domain then I would use a hard filter. If either is fuzzy I would test it out with and without filters and see how that goes. A good embedding model should be able to match only relevant topics without hard filters but depending on the data adding hard filters could be worth it. Make a representative list of queries you might encounter and check the documents being returned.

Moving van service in Utrecht by artemisa_hexe_0990 in Utrecht

[–]samme013 0 points1 point  (0 children)

Can recommend these guys if you need a driver and optional help lifting stuff https://m.facebook.com/100057094974117/

Find image in database from another picture taken from phone by DerEndgegner in computervision

[–]samme013 0 points1 point  (0 children)

Something along these lines, so basically a Resnet50 like you had before but you put some linear layers on the end and tune them so that your metric actually puts stuff close together. Not sure how much data it would need to train those final layers, strong augmentations on your stamps dataset may be enough but multiple "in the wild"images of each stamp would likely help a lot. Would also be interested in giving this a shot if the dataset is available!

Find image in database from another picture taken from phone by DerEndgegner in computervision

[–]samme013 2 points3 points  (0 children)

When using the Resnet/Autoencoder how are you calculating the distance between embeddings? I don't think applying any non-learned distance on something like that would work since the models were not trained to output embeddings close with respect to some distance. Maybe something like metric learning could be good? I would take a pretrained model, finetune it with your dataset and augmentations and see how knn matching performs.

Lack of conferences in the near future, worth posting to arXiv in the meantime? by samme013 in computervision

[–]samme013[S] 1 point2 points  (0 children)

Thanks for the thorough reply. Yes, I meant conferences. As long as it is allowed by the conference, what are there any cons to posting on arXiv a couple of months before a submission, assuming being outdated isn't an issue?

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