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In a Training Loop 🔄
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Naphula

Naphula
cattoroboto's profile picture chant44's profile picture Linkram's profile picture
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AI & ML interests

Building new model tools, merges (7B-24B), ablations, finetunes, and datasets.

Recent Activity

liked a model about 7 hours ago
DeathGodlike/DarkArtsForge_Asmodeus-24B-v2_EXL3
liked a model about 8 hours ago
Nabbers1999/MS-24B-Bathory-GRPO
reacted to Parveshiiii's post with 🔥 about 20 hours ago
Just did something I’ve been meaning to try for ages. In only 3 hours, on 10 billion+ tokens, I trained a custom BPE + tiktoken-style tokenizer using my new library microtok — and it hits the same token efficiency as Qwen3. Tokenizers have always felt like black magic to me. We drop them into every LLM project, but actually training one from scratch? That always seemed way too complicated. Turns out it doesn’t have to be. microtok makes the whole process stupidly simple — literally just 3 lines of code. No heavy setup, no GPU required. I built it on top of the Hugging Face tokenizers library so it stays clean, fast, and actually understandable. If you’ve ever wanted to look under the hood and build your own optimized vocabulary instead of just copying someone else’s, this is the entry point you’ve been waiting for. I wrote up the full story, threw in a ready-to-run Colab template, and dropped the trained tokenizer on Hugging Face. Blog → https://parveshiiii.github.io/blogs/microtok/ Trained tokenizer → https://huggingface.co/Parveshiiii/microtok GitHub repo → https://github.com/Parveshiiii/microtok
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Organizations

Occult AI's profile picture DarkArtsForge's profile picture EldritchLabs's profile picture Naphula-Archives's profile picture 24B-Suite's profile picture