Tonic
Β·
AI & ML interests
π€Making robots to help people learn things quicker π©π»βππ
Recent Activity
reacted to RDTvlokip's post with π about 11 hours ago I finally changed the architecture of my 15M French LLM. It worked. Then I almost fooled myself about how much and catching that was the real win.
After proving last time that architecture is a threshold, not a lever, I got stubborn: could I change how the model learns? Four honest attempts, Lion, a sharper AdamW Ξ²2, multi-token prediction, LayerScale. Four failures. The bottleneck wasn't the learning rule either.
So I changed the shape of the computation instead: loop the same transformer blocks 4Γ, deeper reasoning, zero added parameters. It beat the baseline on perplexity, the first thing in the whole project to move that number. Then I added my own twist: let each token decide how deep to think, halting on its own entropy.
My first evaluation was spectacular. Coherence up 65%. Hallucinated names down 62%.
It was noise.
Eight prompts, one seed. I re-ran on 50 prompts Γ 200 tokens and watched the gains shrink to "modest" and on out-of-domain prompts, recurrence actually made things worse. No universal winner. And none of it is new: it's Adaptive Computation Time (2016), the Universal Transformer (2018), and LoopViT (2026), recombined and measured honestly.
The real lesson:
A number from 8 prompts is a rumor. The eval harness that kills your own best result is worth more than the result it kills. Cite your lineage. Stay preliminary until multiple seeds say otherwise.
The three models are live. The write-up is honest about every caveat π
π https://huggingface.co/blog/RDTvlokip/teaching-a-15m-french-llm-to-think-deeper View all activity Organizations
Tonic/sharing-stuff-with-frens
Tonic/l-operator-instruct
Tonic/voxtral-finetune-20250913_145448
Updated β’ 4
Tonic/sending_files_online_for_my_friends
Updated
Image-Text-to-Text
β’ Updated β’ 1
Image-Text-to-Text
β’ Updated β’ 2
Image-Text-to-Text
β’ 2B β’ Updated Text Generation
β’ 21B β’ Updated β’ 78
β’ 7
Tonic/gpt-oss-20b-multilingual-reasoner
Text Generation
β’ Updated β’ 6
β’ 2
Tonic/gpt-oss-multilingual-reasoner
Updated β’ 1
Tonic/petite-elle-L-aime-3-sft
Text Generation
β’ 3B β’ Updated β’ 14
β’ 1
Tonic/c4ai-command-a-03-2025-4bit_nf4_no_double
Text Generation
β’ 113B β’ Updated β’ 12
Tonic/c4ai-command-a-03-2025-4bit_fp4
Text Generation
β’ 113B β’ Updated β’ 9
Tonic/c4ai-command-a-03-2025-4bit_nf4_double
Text Generation
β’ 114B β’ Updated β’ 9
Translation
β’ 3B β’ Updated β’ 228
β’ 7
Translation
β’ 3B β’ Updated β’ 9
β’ 5
Translation
β’ 3B β’ Updated β’ 6
β’ 1
Tonic/climate-guard-toxic-agent
Text Classification
β’ 0.1B β’ Updated β’ 9
β’ 1
Text Generation
β’ Updated β’ 2
Updated β’ 17
Tonic/video-swin-transformer
Updated β’ 19
β’ 3
Updated β’ 3
Tonic/paligemma-3b-pt-896
Updated β’ 1
Updated β’ 2
β’ 5
Question Answering
β’ Updated β’ 1
β’ 8
Updated β’ 1
Updated β’ 3
β’ 10
Reinforcement Learning
β’ Updated β’ 1