txgsync

u/txgsync


r/SillyTavernAI•

With oMLX, it’s possible to have fast, local, and quite decent quality roleplay in SillyTavern using Gemma 4 models across hundreds to thousands of turns (depending upon response and input length) over contexts extending 262,144 tokens. But SillyTavern's stock settings change early bytes of your prompt on every turn. This busts prefix caches, which prevents oMLX from reusing any of the work it did on the previous turn and results in ever-increasing waits for tokens the longer the conversation goes. Here are six UI changes take measured cache reuse from 0% to ~99%+ (for sufficiently-long contexts) cutting per-turn wait times dramatically from minutes to seconds.

And I've found Gemma

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r/LocalLLaMA•

I downloaded Qwen3-Coder-30B-A3B-Instruct this morning and it surprised me. The model wrote a working Snake game on the first try.

Here's what I did:

  1. Converted the model to MLX format with one command:mlx_lm.convert --hf-path Qwen/Qwen3-Coder-30B-A3B-Instruct --mlx-path ~/models/Qwen3-Coder-30B-A3B-Instruct.mlx --q-group-size 64
  2. Set up a symlink for LM Studio (you can also use mlx_lm.chat)
  3. Gave it a simple prompt: "Write a snake game in python."
  4. Created a Python environment and ran the code:python3 -m venv ./snake && . ./snake/bin/activate && pip install pygame && python ./snake

The results:

  • 56 tokens per second at full 16-bit precision
  • 0.17 seconds to first token
  • To
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r/ElectricUnicycle•

I bought the non-“pro” for $2200 18 months ago. Just $999 right now. Wild sale, akin to their $1399 V13 — a $4,000 wheel — a few days ago.

Other than the tire change being a PITA, I’ve loved riding my V12HT on multi-modal commutes. Fits underneath on coaches, in the luggage rack by my feet on the train, and enough range at my typical riding speeds to go 50 miles between charges on the weekend.

https://store.inmotionworld.com/products/inmotion-v12pro

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