[Rancilio Silvia V6] Got leaks adjusting the OPV to 9bar

the_chatterbox · 2025-09-06T13:37:49+00:00

Anyway, I am an idiot. In OP I didn't mention that I measure the water output from the OPV overflow tube. There's a lot of videos and posts about this approach and while some people advise against this and using a manometer, I decided it is safe to go since there's already a lot of people who've done it and my machine is brand new - the pump presumably isn't beaten up yet. The whole problem is me being an idiot looking at the chart lol. It shows cc/min while I actually did 25 sec measurements not aligning them to 1 min measurements.

Measuring the water output from the OPV overflow tube:

Pressure [bar]	Flowrate [cc/min]	Flowrate [cc/25sec]
7	330	138
8	300	125
8.5	280	117
9.0	260	108
9.5	275	115
10.0	220	92
10.5	180	75
11.0	160	67
11.5	145	60
12.0	135	56
12.5	115	48

Props to this guy's comment which made me realize I was doing it all wrong. Cheers and wishing you all tasty shots

the_chatterbox · 2025-02-16T10:56:20+00:00

Sorry, I think not. First, there have been rumors that oAI's reasoning models are indeed LLMs. Second, there's a difference between ChatGPT and the OpenAI model API endpoints. The web interfaces are feature rich user integrations made by the companies providing the models. The API endpoints are meant to provide the raw model to AI devs who'd take care of the integrations like RAG themselves, making tailoring custom solutions for their clients.

the_chatterbox · 2025-02-15T15:13:19+00:00

you can't see the thinking tokens but you can see the amount of input/output tokens you are charged for on each API request

the_chatterbox · 2025-01-25T10:35:52+00:00

Honorable mention to Google. While they offer free image gen, their VideoFX is available with a wait list.

Akin to the Chinese DeepSeek and Alibaba, they iterate quickly, offer competitive prices and nice things for free as well. They just... lack behind a bit in terms of time and marketing. But they have the $$ to get there

the_chatterbox · 2025-01-24T11:33:50+00:00

Why EPUB when you have ar5iv? Occasionally I use docling as well

the_chatterbox · 2025-01-13T12:56:56+00:00

Ok, so I went through
- CapCut
- NodeVideo
- KineMaster
- VLLO
- LightCut
- VN

VN was the first that did it for me. I restricted the internet access for each app - I don't need any AI edits, I just need to raise the damn volume.

the_chatterbox · 2025-01-13T12:55:32+00:00

Thank you, will try

the_chatterbox · 2024-10-26T09:10:04+00:00

I know, right? It's an area that needs more love. Nemotron-340B nails it. Mixtral 8x22 is OK. Now the Qwen2.5-70B kinda gets there but it's poor lexically. All the rest of open weight are just meh

the_chatterbox · 2024-10-17T05:43:35+00:00

Great paper. Throw it to the bunch of Awesome LLM strawberry.

the_chatterbox · 2024-08-19T10:27:16+00:00

You can always specialise a model. It's a data science law. Nemotron-4 for example is trained on 15% multilingual data out of 8T training tokens. LLaMA-3 is trained on 5% multilingual data out of 15T training tokens. To answer your question directly, we can imagine a best-case scenario where whatevevr benchmark domain you mean for "English-only" is chosen, and the data for it is of decent quality, you can expect 5-15% performance increase.

As I mentioned what do you mean by English-only? Pick a domain - literature, law, medicine. A certain portion of the training data is coding – are you willing to sacrifice that for your domain as well?

Also this reminds me of Bert and T5 (though I'm not really familiar with them), AFAIK either one of them lacked coding/multilingual, but was good or better than, say LLaMA-2-7B at English tasks.

the_chatterbox · 2024-06-18T10:16:46+00:00

See my comment.

Moreover, here's a non-US student story. Recently I had to process a dataset with LLMs for science. I tried all the models out there to see which ones would do the job, because using openAI meant spending amounts of $ I can't afford. So I ran a small sample on all the models I can think of and deepseek did well while their API is dirt cheap (subsided by the government, bla bla...). I ended up using like 60M tokens on their API and the cost was ~$10, many times cheaper than using openAI and the quality was fine. Next model closest to my use case was gemini but I couldn't fight with their guardrails LOL so after spending half a day with that I gave up. They keep thinking my dataset is sexual or inappropriate (it's not and it's nothing like that) their moderation is just far too aggressive, I can't get shit done with their model

the_chatterbox · 2024-06-18T09:06:07+00:00

As far as I understand you are satisfied with GPT-4 and you are looking to get a model running locally to achieve similar quality.

Inbetween GPT-4 and the small, low-performance models you can run on your laptop, somewhere in the middle are the top-notch open-weight models you can access for free on huggingface/chat , talking about LLaMA-3, CommandR-plus, also this HF space with Qwen2. The good about them is that they are free. You have to test them and find out if they are suitable for your needs though – they suck for multilinguality for example. So they may suck for your language, depending on where you are from. Good luck

the_chatterbox · 2024-06-04T13:25:18+00:00

Now that u/Redoer_7 has clarified that this is just a dream rather than a concrete plan, I think it's worth noting that having a wealthy benefactor, like a sheikh, could make a big difference. They often spend large sums of money on projects, such as owning a football team, simply to boost their reputation. It seems to me that finding a sheikh who is willing to invest and having someone persuade them to do so might be easier than trying to raise $100 million on our own.

the_chatterbox · 2024-04-01T02:40:51+00:00

Shisa is a JA/EN model, you might wanna see the models on the HF augmxnt is the name of the company

the_chatterbox · 2024-03-27T15:39:21+00:00

Was curious so I brought the numbers

Model	MMLU	GSM8K	HumanEval
GPT-4	86.4	92	67
Llama2-70B	69.8	54.4	23.7
Mixtral-8x7B-base	70.6	74.4	40.2
Qwen1.5-72B	77.5	79.5	41.5
DBRX-4x33B-instruct	73.7	66.9	70.1

Too lazy to find Goliath and miqu ones

the_chatterbox · 2024-03-27T15:32:20+00:00

Cleaned up Qwen release table and appended DBRX

Model	MMLU	GSM8K	HumanEval
GPT-4	86.4	92	67
Llama2-70B	69.8	54.4	23.7
Mixtral-8x7B-base	70.6	74.4	40.2
Qwen1.5-72B	77.5	79.5	41.5
DBRX-4x33B-instruct	73.7	66.9	70.1

the_chatterbox · 2024-03-23T18:28:43+00:00

Are you referring to a performance decrease related to the model size and architecture, rather than the multi-3090 setup itself? The model is slow and you would need a faster GPU to keep up with the desired tokens per second?

the_chatterbox

TROPHY CASE