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takarajordan

AI & ML interests

Chief AI Officer @takara.ai. Diffusion, Inference optimisation and all things MultiModal.

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Reacted to luigi12345's post with ๐Ÿ‘€ about 3 hours ago
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1193
MinimalScrap
Only Free Dependencies. Save it.It is quite useful uh.


!pip install googlesearch-python requests
from googlesearch import search
import requests
query = "Glaucoma"
for url in search(f"{query} site:nih.gov filetype:pdf", 20):
    if url.endswith(".pdf"):
        with open(url.split("/")[-1], "wb") as f: f.write(requests.get(url).content)
        print("โœ…" + url.split("/")[-1])
print("Done!")

replied to averoo's post about 4 hours ago
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Wow this is a really great idea man, the UI needs some work but I really prefer having the summaries on the front page!

If you need some help on it let me know.

Reacted to merve's post with ๐Ÿš€ 3 days ago
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2545
your hugging face profile now has your recent activities ๐Ÿค—
New activity in donut-earth/proof 4 days ago

Another fake image

#4 opened 4 days ago by qkasriel

Fake image in the dataset.

1
#1 opened 4 days ago by qkasriel
Reacted to m-ric's post with ๐Ÿš€ 4 days ago
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758
๐—ก๐—ฒ๐˜„ ๐—น๐—ฒ๐—ฎ๐—ฑ๐—ฒ๐—ฟ๐—ฏ๐—ผ๐—ฎ๐—ฟ๐—ฑ ๐—ฟ๐—ฎ๐—ป๐—ธ๐˜€ ๐—Ÿ๐—Ÿ๐— ๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—Ÿ๐—Ÿ๐— -๐—ฎ๐˜€-๐—ฎ-๐—ท๐˜‚๐—ฑ๐—ด๐—ฒ: ๐—Ÿ๐—น๐—ฎ๐—บ๐—ฎ-๐Ÿฏ.๐Ÿญ-๐Ÿณ๐Ÿฌ๐—• ๐˜๐—ผ๐—ฝ๐˜€ ๐˜๐—ต๐—ฒ ๐—ฟ๐—ฎ๐—ป๐—ธ๐—ถ๐—ป๐—ด๐˜€! ๐Ÿง‘โ€โš–๏ธ

Evaluating systems is critical during prototyping and in production, and LLM-as-a-judge has become a standard technique to do it.

First, what is "LLM-as-a-judge"?
๐Ÿ‘‰ It's a very useful technique for evaluating LLM outputs. If anything you're evaluating cannot be properly evaluated with deterministic criteria, like the "politeness" of an LLM output, or how faithful it is to an original source, you can use LLM-judge instead : prompt another LLM with "Here's an LLM output, please rate this on criterion {criterion} on a scale of 1 to 5", then parse the number from its output, and voilร , you get your score.

๐Ÿง But who judges the judge?
How can you make sure your LLM-judge is reliable? You can have a specific dataset annotated with scores provided by human judges, and compare how LLM-judge scores correlate with human judge scores.

๐Ÿ“Š Before even running that benchmark, to get you started, there's a new option to get you started: a leaderboard that measures how well different model perform as judges!

And the outcome is surprising, models come in quite different orders from what we're used to in general rankings: probably some have much better bias mitigation than others!

Take a deeper look here ๐Ÿ‘‰ https://huggingface.co/blog/arena-atla
posted an update 4 days ago
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817
First post here goes!

takarajordan/CineDiffusion

Super excited to announce CineDiffusion๐ŸŽฅ, it creates images up to 4.2 Megapixels in Cinematic ultrawide formats like:
- 2.39:1 (Modern Widescreen)
- 2.76:1 (Ultra Panavision 70)
- 3.00:1 (Experimental Ultra-wide)
- 4.00:1 (Polyvision)
- 2.55:1 (CinemaScope)
- 2.20:1 (Todd-AO)

More to come soon!!

Thanks to @John6666 and @Resoldjew for your early support <3

And thanks to the team at ShuttleAI for their brand new Shuttle-3 model, what an amazing job.

shuttleai/shuttle-3-diffusion
New activity in CohereForAI/aya_expanse 26 days ago