> "Am I talking to real person?"
>> "Yes, you are talking to a real person..."
Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context
311–320 of 358 posts
Re: Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context
#312Is the Phind model available outside of phind.com as an api or are weights available for fine tuning?
Re: Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context
#313Earlier quoted context omitted.
As a user, i perfer getting the right response compared to the thing spitting out a link. (not saying phind is bad). Lets focus on getting llm right before nerfing it in its baby stages.
Give me the citations every day of the week. The source of information matters. For example, I don't rely on any ZFS info or opinions I find online if I can't verify it came from a contributor or highly reputable person that has a lot of experience with ZFS. If you want to show the warts of all these LLMs, ask it about ZFS if you know enough to spot the commonly parroted misinformation that plagues the internet. IMHO…
Funny you bring up ZFS specifically. I embarrassed myself a couple weeks ago by parroting something GPT-4 told me about ZFS to someone on reddit, which turned out to be completely wrong.
Re: Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context
#314I tried my standard "trick" question I use for LLMs: "Give me five papers with code demonstrating the state of the art of machine learning which uses geospatial data (e.g. GeoJSON) as both input and output." There is no such state of the art. My hand-wavey understanding is that GIS data is non-continuous, which makes it useless for transformers, and also contextual, which makes it useless for anything else. Will defe…
> the state of the art of machine learning which uses geospatial data (e.g. GeoJSON) as both input and output
> There is no such state of the art
Some GIS work uses vector data: points/lines/polygons representing features (e.g., the location of roads or the outlines of buildings), which can be stored in formats like GeoJSON or WKT. But other work uses remote sensing data/satellite imagery that can be stored in raster formats like GeoTIFF - essentially TIFF image files with additional information stored to georeference them.
You can totally do machine learning on satellite imagery where both the input and output are geospatial data (e.g. to categorise land use - the inputs are multispectral images and the outputs can be images where the value of each pixel represents the identified land use).
You can also use machine learning for tasks like building footprint detection/delineation (e.g., [1]) based on satellite imagery. The output from such a pipeline can be a set of polygons, which could be saved as GeoJSON.
I'd consider either of theses to be examples of "machine learning which uses geospatial data (e.g. GeoJSON) as both input and output".
[1]: https://azure.microsoft.com/en-us/blog/how-to-extract-buildi...
Re: Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context
#315This model clearly makes a much better search engine than google/kagi/bing/etc.
I've been searching for an obscure connector -- the 8-pin connector you'd find on the cable that delivers power to a GPU, but in a form that can be wave-soldered. I've spent hours searching all the big electronics distributors -- no luck. This thing found it in seconds.
https://www.phind.com/search?cache=a7e9u5l5aw1r8ufls0icpb63
This is a very common connector but in a highly unusual form-factor. Molex refuses to make wave-solderable versions of it.
Edit: the first link does not lead directly to the obscure connector, but to the website of a company that does sell it. Here is the obscure connector: https://www.moddiy.com/products/Special-Mini-Low-Profile-ATX... Maybe it just got lucky.
On the other hand, it hallucinated the crap out of a very straightforward question "how do i connect the wake# pins when bifurcating a pcie port?" -- the answer is that it's an open-drain pin so (unlike the clock pins which need a buffer chip) you just wire them both together:
https://www.phind.com/search?cache=zf9witr85q740l4s3vjwzf01
Then it tried to write a bunch of code for an obviously-not-coding question. Not so great.
Re: Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context
#316Earlier quoted context omitted.
ChatGPT 4 with web browsing: https://chat.openai.com/share/19a425b5-ed37-469e-860d-65ee70... ChatGPT 4 without web browsing: https://chat.openai.com/share/7e11b4a6-52f2-441a-8614-7266c3...
ChatGPT 4 seems to be better than it was when I was using it (mere months ago)!
Yeah, all of OpenAI's stuff gets better much quicker than I'm used to. So does the general performance ceiling of all open source models, even if individual models don't improve as much.
Re: Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context
#317Earlier quoted context omitted.
That conclusion is based on their benchmarks. I'm not interested in those. I'm interested in community benchmarks, like those we're seeing in the comments. Lo and behold, GPT-4 is still king. The claims of any company should be taken with exactly a pinch of salt.
that benchmark(HumanEval) is some public benchmark built by others.
That kind of benchmark is a lot more reliable for models published before the benchmarks; models published afterwards have more opportunity to "study to the test". That's especially a concern when a company explicitly uses its score on that benchmark as a marketing point.
Re: Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context
#318This model clearly makes a much better search engine than google/kagi/bing/etc. I've been searching for an obscure connector -- the 8-pin connector you'd find on the cable that delivers power to a GPU, but in a form that can be wave-soldered. I've spent hours searching all the big electronics distributors -- no luck. This thing found it in seconds. https://www.phind.com/search?cache=a7e9u5l5aw1r8ufls0icpb63 This is a…
The first reference returned was a plug intended to be used with wire connections. Not wave solderable at all. The other two returned were to a site search that returned nothing relevant and a large section of a manufacturers web site respectively.
Re: Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context
#319This model clearly makes a much better search engine than google/kagi/bing/etc. I've been searching for an obscure connector -- the 8-pin connector you'd find on the cable that delivers power to a GPU, but in a form that can be wave-soldered. I've spent hours searching all the big electronics distributors -- no luck. This thing found it in seconds. https://www.phind.com/search?cache=a7e9u5l5aw1r8ufls0icpb63 This is a…
The first reference returned was a plug intended to be used with wire connections. Not wave solderable at all. The other two returned were to a site search that returned nothing relevant and a large section of a manufacturers web site respectively.
Correction, the first link was to a vendor who does in fact sell the obscure connector:
https://www.moddiy.com/products/Special-Mini-Low-Profile-ATX...
I'm just elated I finally found out where to buy these damn things from.
Re: Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context
#320I am a heavy user of GPT4, and Phind was surprisingly able to match GPT4 on several initial programming tasks I gave it. Given the large context window of Phind, it will likely be able to outperform GPT4 for some tasks. That is quite an accomplishment, I am impressed
FWIW The default context window of GPT-4 via ChatGPT is about to change to 32k.
Source?