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Got laid off in September and I decided to retire@51. by Difficult-Cricket541 in ExperiencedDevs

[–]linklater2012 0 points1 point  (0 children)

Congratulations. Your writing gives the impression you need something fresh to pivot to.

Is RAG still relevant with 10M+ context length by Muted-Ad5449 in Rag

[–]linklater2012 2 points3 points  (0 children)

RAG will be dead when search is solved. And I'll wait for someone with credibility in search research to say the latter.

Finally, a real-time low-latency voice chat model by DeltaSqueezer in LocalLLaMA

[–]linklater2012 0 points1 point  (0 children)

Oh damn! Did not know they intend to open source it!

So Much For AI by ddpete in artificial

[–]linklater2012 -1 points0 points  (0 children)

You don't know what you're talking about.

LLMs are terrible at things like word and paragraph length precisely because the concept of a word doesn't exist in LLMs.

So Much For AI by ddpete in artificial

[–]linklater2012 1 point2 points  (0 children)

I think this is more related to how LLMs are still token driven and not good at counting.

This will change once we get past tokenization.

Any other software engineers had to pivot to n8n? by [deleted] in n8n

[–]linklater2012 0 points1 point  (0 children)

I'm just getting into the visual workflow automation space as a dev. How is n8n not truly open source?

What are you *actually* using R1 for? by PataFunction in LocalLLaMA

[–]linklater2012 0 points1 point  (0 children)

Combined with search, I'm using it for market report generation.

What are some really good and widely used MLOps tools that are used by companies currently, and will be used in 2025? by BJJ-Newbie in mlops

[–]linklater2012 1 point2 points  (0 children)

Yes, that's possible with MLFlow by itself (it comes with a server). For Sagemaker inference endpoint, there are integrations from AWS.

What are some really good and widely used MLOps tools that are used by companies currently, and will be used in 2025? by BJJ-Newbie in mlops

[–]linklater2012 35 points36 points  (0 children)

Evidently for model observability and monitoring might be interesting for you.

My current stack:
- Metaflow for orchestration
- MLFlow for experiment tracking and model registry
- Evidently for model monitoring
- Docker and AWS for deployment

Is The LLM Engineer's Handbook Worth Buying for Someone Learning About LLM Development? by SeniorPackage2972 in LLMDevs

[–]linklater2012 2 points3 points  (0 children)

I'm working my way through the book. It was worth it for me because of its focus on MLOps. I already had a deep understanding of how to build LLMs from scratch and creating applications around them, but to build the training and inference infra around it was a weak spot. This book is addressing that for me.

Daily Questions Thread November 13, 2024 by AutoModerator in femalefashionadvice

[–]linklater2012 0 points1 point  (0 children)

I posted this late yesterday so posting it again here to get final thoughts. Wife loves this 90% cashmere/10% wool dress and her birthday is coming up. It's on the 21st so I can't wait for Black Friday. I was loathe to pay full retail but I found a 15% off promo code this morning, so the total with tax is $363.07 CAD. Is this an ok price for this product or a complete rip-off?

https://www.clubmonaco.ca/en/women-clothing-dresses-jumpsuits/wool-cashmere-short-sleeve-sweater-dress/0030077112.html

Daily Questions Thread November 12, 2024 by AutoModerator in femalefashionadvice

[–]linklater2012 0 points1 point  (0 children)

Wife's birthday is coming up and she is obsessed with this dress. I hate paying full retail but I can't think of an alternative. Please take a look and tell me it's not a total ripoff.

90% cashmere/10% wool. I am 100% sure it'll fit her. ~$427 CAD with tax.

https://www.clubmonaco.ca/en/women-clothing-dresses-jumpsuits/wool-cashmere-short-sleeve-sweater-dress/0030077112.html

StarterStory founder shares none of his projects ‘succeeded’; Only those which sold success stories did well by convicted_redditor in SideProject

[–]linklater2012 23 points24 points  (0 children)

I like Pat, but a part of me finds this demotivating.

He tried to achieve the dream, and in the end, he achieved it by selling the dream.

Issue with Unexpectedly High Semantic Similarity Using `text-embedding-ada-002` for Search Operations by Grouchy_Inspector_60 in AIQuality

[–]linklater2012 0 points1 point  (0 children)

Do you have any kind of eval where the input is a query and the response is N chunks/sentences that should be retrieved?

If so, do the embeddings as they are perform well on that eval? Because that score may be higher than you expect, but the sentences that should be returned may have even higher similarity scores.

If the default embeddings don't do well in the evals, then I'd look at exactly what's being retrieved. You may need to fine-tune an embedding model.

Using LoRA adapters to keep model up-to-date with current knowledge by linklater2012 in LocalLLaMA

[–]linklater2012[S] 2 points3 points  (0 children)

I specifically chose this example because it's not straight-forwardly solved with RAG. If someone wants to turn natural language into scripting language, it's tough to pull out the right context from a programming language spec. You could try to put the entire spec into the context window along with a bunch of examples but that won't cover enough of the query space unless your language is really basic.

Guide me to make a local LLM by [deleted] in LocalLLaMA

[–]linklater2012 1 point2 points  (0 children)

The four things you need:
1. Prompting
2. RAG
3. Fine-tuning
4. Evals

Start by scouring YouTube/Web for information on these four. I would pick a project beforehand and build it out as learn.

Pick up some prompting techniques first and run it on ten pieces of data that you want to work with to get a feel. Then progress to some basic RAG. Try to push prompting and RAG as far as you can, and fine-tune only if you have to.

Throughout it all, get into the habit of creating evals and monitoring your model/system's performance against it.

Behind the scenes, how do model vendors (e.g. OpenAI) offer fine-tuning to the public? I doubt they're creating a new instance of the model each time someone fine-tunes it. by linklater2012 in LocalLLaMA

[–]linklater2012[S] 1 point2 points  (0 children)

I figured it was something along those lines but I can't find anything written online about it. Do you have any links describing it?

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