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salma-remyx

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https://remyx.ai

smellslikeml

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reacted to davidjbrewer's post with 🔥 about 1 hour ago

We use North Mini Code 1.0 locally (Ollama, q4_K_M) with OpenCode as our primary coding model. It's been the strongest small agentic coder we've tested. The one gap in daily work is that we can't paste screenshots of errors, terminal output or UI bugs into a session, since the model is text-only. Switching to a separate vision model mid-task breaks the workflow. Is native image input planned for a future North Mini Code release, for example by building in North Micro Vision's encoder? We'd test an early build.

reacted to Nymbo's post with 🔥 3 days ago

Introducing Live Subtitles, a Windows tray app that captions anything your PC is playing and isn't butt ugly. live and fully local. It runs NVIDIA's Parakeet-unified 0.6B on your GPU, puts words on screen about 0.36 s after they're spoken, and uses less than one CPU core while it does it. I built a benchmark for it out of real TV, podcasts, earnings calls and a short film. Parakeet-unified scores 12.1% WER there, close to Nemotron streaming's 11.4%, but with much better punctuation and lower worst-case delay. Once a caption is on screen it never gets rewritten or jumps lines, and the overlay disappears when nobody's talking. No NVIDIA GPU? It falls back to Nemotron on the CPU, which also covers 30 other languages. It can also write SRT/ASS/VTT files for videos on disk. Windows only. The GPU engine needs an NVIDIA card with ~3.2 GB of free VRAM. MIT. Edit: the first version of this post said 13.8% vs 17.0%. The replies caught a scoring bug (numbers weren't normalized), so the benchmark now uses Whisper's English normalizer and the numbers above are re-scored. Get it here: https://github.com/Nymbo/Live-Subtitles Models: https://huggingface.co/nvidia/parakeet-unified-en-0.6b https://huggingface.co/nvidia/nemotron-speech-streaming-en-0.6b

reacted to AC-723's post with 🔥 6 days ago

MixtureKit 1.1 is out 🚀 Turn the fine-tuned models you already have on the Hub into one Mixture-of-Experts model. pip install mixturekit Give it a base model and its fine-tunes (math, code, a language...). MixtureKit merges them, trains the routers that pick an expert for every token, and shows you what they learned: 🧩 3 composition methods behind one YAML: Branch-Train-MiX, Mixtral-style MoE, Branch-Train-Stitch 💻 Merges stream the experts tensor by tensor: they run on a laptop 🎯 Router training with a load balancing loss, optional LoRA 🔍 A routing visualizer: in the image, two Nile 4B experts split a mixed Egyptian sentence, Arabic script to one expert, Arabizi to the other 📦 Export to Mixtral / Qwen3-MoE, then GGUF for llama.cpp 🤗 mixturekit push uploads the result with a generated model card ✅ Llama 3, Qwen 2.5/3/3.5, Gemma 3/4 experts; tested on transformers 4.52 to 5.19 In the paper, a BTX model built with MixtureKit outperforms the dense baselines on multiple benchmarks. ▶️ Build, train and visualize an MoE in 15 min on a free Colab GPU: https://colab.research.google.com/github/MBZUAI-Paris/MixtureKit/blob/main/examples/build_your_own_moe.ipynb 📄 Paper: https://huggingface.co/papers/2512.12121 💻 Code: https://github.com/MBZUAI-Paris/MixtureKit 📚 Docs: https://mbzuai-paris.github.io/MixtureKit/ Work with @guokan-shang and @oe2015 at @MBZUAI-Paris 🙌

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