KP @kexecv
LLMs, Infosec, Programming, Science & Technology kuldippatel.dev Singularity Joined June 2012-
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Excited to share a new open-source project I made for quickly testing LLM architectures! It's heavily based on nanoGPT and other OSS repos. Currently supports single GPU only. This is separate from the YALM project, which uses the HuggingFace Trainer. github.com/kp7742/gpt-tra…
There are rumours of TikTok coming back to India. Almost everybody is talking about what they will make once it comes. I'm rather interested in the juicy training data it will generate.
I'm so mid.
That’s what we call quality content, a must read.
deepmind and openai cooked hard but somebody got sidelined 😏
deepmind and openai cooked hard but somebody got sidelined 😏
Take: Chain of Thought is a misleading name. It's really a "scratchpad". "Thoughts" are internal activations Imagine you're solving a problem and have a scratchpad. Reading the pad gives me info! You *can* avoid writing down key thoughts. But it's a handicap. Real but fallible
I have been busy with understanding GRPO. Experimenting different ways and tuning parameters to bootstrap reasoning in small models without additional training. Getting some promising results with SmolLM2-135M. Also YALM-80M pretraining is delayed, but still on the roadmap.
Just tried Instruction tuning today! I finetuned SmolLM-135M base model using a custom chat format on the SmolTalk dataset for one epoch. I even built a Gradio web chat for easy testing of models. It's great for simple instructions, but not so much for math and tool calls!
Tbh, this delay is definitely due to the Kimi K2 release, but not for the reason many people believe. The release of such a SOTA open model would steal the spotlight from their model release. Regardless of its size, everyone would compare it to larger open models. They were…
Tbh, this delay is definitely due to the Kimi K2 release, but not for the reason many people believe. The release of such a SOTA open model would steal the spotlight from their model release. Regardless of its size, everyone would compare it to larger open models. They were…
Attention or linear models alone cannot fix everything; hybrid models are the way forward.
Tokenizer test results are here! RTL digit tokenization didn't do as well as expected, maybe due to inconsistency. Interestingly, individual digits performed best, likely because of better compression. SuperBPE, despite a higher training loss, did great on benchmarks.
Tokenizer test results are here! RTL digit tokenization didn't do as well as expected, maybe due to inconsistency. Interestingly, individual digits performed best, likely because of better compression. SuperBPE, despite a higher training loss, did great on benchmarks. https://t.co/5CGcWLoCip
Tested Grok-4 on OpenRouter because couldn't afford SuperGrok🫣. Still the same old Grok3 base model, although I expected more. Good at following instructions and takes quite open stand, but I wish I could see its "thinking" process.
SuperBPE is quite interesting! It effectively compresses English and Code with just a 32k vocab. But it doesn't compress non-English languages like Hindi that well. Probably I should have tested bigger vocab size but I was resource constraint.
Added SuperBPE with digit tokenization tests. It had mixed results; some positive and some negative. I'll share more details soon! Paper: arxiv.org/pdf/2503.13423
Added SuperBPE with digit tokenization tests. It had mixed results; some positive and some negative. I'll share more details soon! Paper: arxiv.org/pdf/2503.13423 https://t.co/G9VjNwkHai
Final test run needs 5 more hours, then i need to quickly publish my eval results! Don't want Grok 4 hype to overshadow them. Exciting AI weekend ahead!
Cute memories 😌

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