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DedeProGames
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AI & ML interests

Agentic & Coding finetune, Decoder-Onlys from Scratch, Image LoRAs, AI Researcher

Recent Activity

updated a dataset about 3 hours ago
DedeProGames/lm-tetris-arena-results
repliedto their post about 11 hours ago
šŸš€ Introducing Kiyo-230M-Instruct ā€” my compact English & Portuguese chat model! 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. šŸ§  229.7M parameters šŸŒ English + Portuguese šŸ’¬ Native chat template for multi-turn conversations šŸ“ 2,048-token context window šŸ“¦ 459 MB of BF16 safetensors weights šŸ”“ 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. Iā€™m excited to share it with people exploring how much we can get from small, accessible models! šŸ¤— Model & full README: https://huggingface.co/DedeProGames/Kiyo-230M-Instruct šŸ’¬ Try the chat demo: https://huggingface.co/spaces/DedeProGames/kiyo-230m-chat Try it in English or Portuguese ā€” Iā€™d love to see your examples, feedback, and experiments!
reacted to theirpost with šŸ¤Æ about 11 hours ago
šŸš€ Introducing Kiyo-230M-Instruct ā€” my compact English & Portuguese chat model! 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. šŸ§  229.7M parameters šŸŒ English + Portuguese šŸ’¬ Native chat template for multi-turn conversations šŸ“ 2,048-token context window šŸ“¦ 459 MB of BF16 safetensors weights šŸ”“ 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. Iā€™m excited to share it with people exploring how much we can get from small, accessible models! šŸ¤— Model & full README: https://huggingface.co/DedeProGames/Kiyo-230M-Instruct šŸ’¬ Try the chat demo: https://huggingface.co/spaces/DedeProGames/kiyo-230m-chat Try it in English or Portuguese ā€” Iā€™d love to see your examples, feedback, and experiments!
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