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reacted to AbstractPhil's post with 🔥 about 9 hours ago
I believe I have a solution for cross-tokenizer chatter and noise, which I've built a prototype repo for this exact tooling dubbed bytelex. https://github.com/AbstractEyes/geolip-bytelex I had a bit of an inspiration recently and built a prototype for a token translation matrix that I called geolip-bytelex, which allows bytewise translation of many different tokenizers into byte format. The goal is to allow comparative distillation from multiple models to simultaneously represent expertise based on input tokens and differentiated teacher/student InfoNCE and MSE training paradigms, while cutting a huge cost of the distillation analysis comparative compute that cross-tokenizer noise will naturally cause when tokenizers are mismatched or incorrect, reducing a large portion of invalidity from the trained systems established by incorrect valuations from the distillations and lora trainings. Bytelex is essentially a byte-wise deconstruction of a tokenizer's state into a preliminary 255 byte language allowing for 10s of thousands of sequences per token to be represented rather than just a few. I'm not the first to try this, however I'm in a unique position due to my creation AlephLM being built entirely by learning it's own lexicon, thus allowing this to be more than experiment and instead a working prototype distillation potential. This can solve a longstanding multi-tokenizer problem that I and many other researchers have been facing, at the cost of setup overhead compute for the preliminary experiments, however the translation matrix I'm planning will potentially solve this problem allowing models to be directly bytewise captured in a more guaranteed methodology through cross-sampled analysis at distillation time in this optimizer state that I'm working out. I've dubbed this distillation loss ByteInfoNCE and the preliminary is showing humongous promise, with that the bytelex is the crux and prototype concept that I'll be expanding and researching further.
reacted to OppaAI's post with 🔥 about 9 hours ago
Here is the longer version of the demo video of Phase 2 - Voice Input and Output Actually the voice input and output were fully implemented and even optimized by removing the heavy GPU-utilizing PyTorch and streamlined the pipelines in the beginning of this month Aug 2026: Phase 2 focus: Ultra-low latency voice I/O with production-grade optimizations. Both ASR and TTS servers with voice cloning are fully functional, and we've completely streamlined the pipeline for real-time performance. ASR (SenseVoice + SileroVAD-onnx) utllizes about 700MB RAM when in use (on-demand) TTS (MioTTS 0.4GB Q4 + C++ inference) pre-occupies about 1.2GB RAM (instead of over 2+ GB before with PyTorch) What's Actually New in Phase 2: ⚡ Production Voice Pipeline: Decoupled LLM + TTS inference with sub-second latency. The ASR→LLM→TTS chain is now optimized end-to-end. 🚀 Zero PyTorch Overhead: Ditched PyTorch dependencies entirely. Now running C++ optimized MioTTS inference + SileroVAD (ONNX) for lightning-fast voice processing—no deep learning framework bloat. 🌍 Seamless Bilingual Voice: English + Japanese in the same sentence, perfect for learning Nihongo alongside your AI companion. 🔧 Full System Optimization: Pre-warmed modules, systemd TTS server loading, and every millisecond shaved off the inference pipeline. 🛠️ Other features include: Barge-in interruption - Speak in the middle of the voice output of AI to interrupt Wake word activation - Wake up AI Waifu using special word/phrase defined in YAML Speaker verification - Supposed to allow only enrolled users' voices for voice input. Need to tighten the model threshold to stop AI from responding to strangers' voices. 👉 GitHub: https://github.com/OppaAI/Aiko-chan 📹Demo: https://youtu.be/VGHBMMFskCQ
reacted to DavidAU's post with 🔥 about 9 hours ago
Qwen3.8 - 27B - COLD FUSION Reduction in thinking tokens to 1/10 to 1/2 "normal Qwen" across all three modes of thinking without loss of detail in thinking or output. Increase in general intelligence too (all 7 critical benches); bench marks posted. Trained using the COLD FUSION method (GAIN + UNSLOTH). NOTE: This is NOT a heretic / uncensored version. MTP and GGUF NEO MAX quants: https://huggingface.co/DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-NM-DAU-NEO-MAX-MTP-GGUF SOURCE: https://huggingface.co/DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1
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