Pranjwal Jha

179 posts

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Pranjwal Jha

Pranjwal Jha

@CatOrange4185

small fish in big pond

New Delhi, India Katılım Temmuz 2024
221 Takip Edilen9 Takipçiler
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Pranjwal Jha
Pranjwal Jha@CatOrange4185·
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Pranjwal Jha
Pranjwal Jha@CatOrange4185·
I miss my phone :cry:
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Ostris
Ostris@ostrisai·
I made a training adapter for @krea_ai Krea2 Turbo that will allow you to train a LoRA on the turbo model directly without breaking down the distillation. This works the same way as my Z-Image turbo adapter.
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Pranjwal Jha
Pranjwal Jha@CatOrange4185·
Such a great model and app congratulations
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Shreyas Arun 🫧
Shreyas Arun 🫧@shreyas_noon·
@levelsio Infrastructure got so cheap that even abuse costs less than a dinner. The real question is why scraping defense isn't a default feature on every hosting platform.
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Pranjwal Jha
Pranjwal Jha@CatOrange4185·
Great things are happening with Google gemini, at this point delete the gemini app and it's codebase
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Junyang Lin
Junyang Lin@JustinLin610·
me stepping down. bye my beloved qwen.
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Qwen
Qwen@Alibaba_Qwen·
🚀 Introducing Qwen-Image-2.0 — our next-gen image generation model! 🎨 Your imagination, unleashed. ✨ Type a paragraph → get a pro slides ✨ Describe a scene → get photoreal 2K magic ✨ Add text → it just works (no more glitchy letters!) ✨ Key upgrades: ✅ Professional typography (1K-token prompts for slides, posters & comics) ✅ 2K native resolution with stunning detail ✅ Flawless text rendering + unified generation/editing ✅ Lighter architecture = faster inference Try it now → chat.qwen.ai/?inputFeature=… Full details → qwen.ai/blog?id=qwen-i…
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Pranjwal Jha
Pranjwal Jha@CatOrange4185·
Microsoft is such a cunt ass company with zero morals
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Pranjwal Jha
Pranjwal Jha@CatOrange4185·
Why do I both making something i should just use perplexity to search fuck my chud ass life
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Pranjwal Jha
Pranjwal Jha@CatOrange4185·
wow what goes on web stays on web, single perplexity tab using 50% of cpu and 1 gb of ram 😂 apparently the model hallucinated forever or something
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Dr Bombay
Dr Bombay@dopdopinder·
@TheLBOguy @CatOrange4185 @ThePrimeagen @beaversteever Then why the fixation to point out syntactical errors? "Did you forget to import a library?" Of course I did, all IDEs automatically add them since ages. Thinking can be tested with psuedo-code or even flowcharts, if you are evaluating thought process. But no, write this document
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Steve the Beaver
Steve the Beaver@beaversteever·
"after 11 technical interviews" "your work has been accepted into our codebase" interviews are getting out of hand
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Dr Bombay
Dr Bombay@dopdopinder·
@ThePrimeagen @beaversteever Why does an "AI-first" company want people to not use AI or even an IDE in 2025? What sick obsession is that with paper/whiteboard/word processor coding? What exactly does that even test?
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Himanshu Kumar
Himanshu Kumar@codewithimanshu·
@iamgrigorev George, that's a brilliant question! Parallelism in PyTorch is crucial for scaling, especially with those frameworks, right?
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George Grigorev
George Grigorev@iamgrigorev·
Curious how frameworks like nanochat actually scale? New blog post: Introduction to Parallelism in PyTorch. Covers async DDP, ZeRO-1/2, FSDP, and TP – with implementations from scratch and practical advice from real runs on different hardware. Even if you are experienced, you’ll likely find something new 👇
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Pranjwal Jha
Pranjwal Jha@CatOrange4185·
All this for a mid ass ui 💔
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pdawg
pdawg@prathamgrv·
Introducing TensorTonic. A platform to actually learn ML by building it. > Practice ML algorithms in LC style > Replicate cutting-edge ML papers > Prepare with company-wise AI interview questions + blogs. v1 is out. accelerating hard. website link in comments.
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Pranjwal Jha
Pranjwal Jha@CatOrange4185·
Just removed conda, I'm all in on uv now
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Pranjwal Jha
Pranjwal Jha@CatOrange4185·
back to this 🥲 I like zed ui more than nvim sort of
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Pranjwal Jha
Pranjwal Jha@CatOrange4185·
@tobyordoxford Hf solves this by not normalising each advantage term by response length but by total completion tokens I believe
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Toby Ord
Toby Ord@tobyordoxford·
I find it fascinating that when DeepSeek used reinforcement learning to train an LLM to reason better, its chain of thought grew linearly (an equal amount per step of training) — and that amount appears to be exactly 1 token longer per step of RL:
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