Hoglet (Ash) PRO
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Built on Kiyo-230M-Preview, a base model I pretrained from scratch, Kiyo now has an instruction-tuned release for everyday conversations, rewriting, and lightweight assistant experiments.
๐ง 229.7M parameters
๐ English + Portuguese
๐ฌ Native chat template for multi-turn conversations
๐ 2,048-token context window
๐ฆ 459 MB of BF16 safetensors weights
๐ Apache-2.0 license
For instruction tuning, I used 165,735 conversations from SmolTalk2, smol-smoltalk, UltraChat, and Dolci-Instruct-SFT. Training ran for two complete epochs with assistant-only loss, using a roughly 70% English / 30% Portuguese token mix.
The updated model card includes benchmarks against SmolLM2-135M-Instruct, BananaMind, and Supra2, plus example conversations, CPU measurements, training details, and a Transformers quickstart.
The results are mixed: Kiyo is competitive with these small models across the reported evaluations, while SmolLM2 and BananaMind score higher on the instruction-following benchmarks. Portuguese was a highlight in the small conversational sanity test.
This is an experimental 230M model, with plenty of room to improve reasoning, factual accuracy, and strict instruction following. Iโm excited to share it with people exploring how much we can get from small, accessible models!
๐ค Model & full README:
DedeProGames/Kiyo-230M-Instruct
๐ฌ Try the chat demo:
DedeProGames/kiyo-230m-chat
Try it in English or Portuguese โ Iโd love to see your examples, feedback, and experiments!
@DedeProGames Why those languages?
@Banaxi-Tech YES! you should. You are the one that inspired me to start making SLMs in the first place. Just because of some controversy doesn't mean you should stop.
yeah def, it might be easier to do one launch for these though... depends i guess.
@Datdanboi25 just wondering why you would prefer the models to be launched when ready instead of in one big launch?
yeah i know, i have my priorities elsewhere
YAY!
also dang it, i was gonna do that lol. How do you build all of these so quickly?
they wont be using NSA i really only heard about NSA after the BGA stuff lol, ive already started training on most of the models to switch, i'll consider it for future models..
the largest I'm going to do for 1.5 is 125M, 150M will be later lol
1. Should I release all Pebble 1.5 models together in one big launch, or release each model individually as it's ready?
2. What would you like to see from the Pebble models? Any specific features, capabilities, or improvements?
3. What would you like to see from basically AI as a company in general?
I'd love to hear your thoughts and suggestions!
actually a longer context does not take up more embedding params. though it sometimes can depending on the positional embeddings used; if you are using RoPE more or less context doesnt affect param count. Out of the most popular types only learned absolute embeddings will always create more params whereas relative learned embeddings will sometimes create more. most others dont.