Run #LLMs# on @NVIDIARobotics #Jetson# without x86 ONNX export. 🚀 #TensorRT-Model-Connect# on reComputer Classic J5012 (#AGXOrin# 64GB, #JP7#.2). @huggingface checkpoint → trtmc build → .bundle on device trtmc run for text gen Tested: #Qwen3-4B# FP16 (~41GB container peak, 64GB recommended) No ONNX hop. Edge LLM/agent ready. Would you use on-device TRTMC for prototyping, or keep ONNX export for production? 👇 Full guide: Get your own one: #NVIDIA# #Jetson# #TensorRT# #TRTMC# #AGXOrin# #EdgeAI# #LLM# #PhysicalAI# #SeeedStudio#

Audio8 TTS Preview 0.1B — now on Hugging Face as ONNX INT8 ~100M params · 11 languages · zero-shot voice cloning Runs on CPU only (~0.4 GB RAM) — no PyTorch, no CUDA 44.1 kHz · OpenAI-compatible API · Apache 2.0 Try it 👇 #TTS# #VoiceCloning# #ONNX# #OpenSource#

I gave a talk last night at DataPhilly on inference in production with ONNX and ONNXRT and the differences between generative and embedding architectures, and where both have a place in information retrieval systems, slides are here.

this TTS model generates speech 167x faster than you can hear it. Supertonic is an on-device TTS engine that runs via ONNX for cross-platform inference. - no GPU - 31 languages - captures every emotion - beats ElevenLabs on speed - runs even on a Raspberry Pi 100% open-source.

English TTS in just 1.4M parameters, vocoder included. PiVo-1.4M runs offline on a CPU with NumPy and ONNX Runtime, without PyTorch. 🤖 🪶 1.4M parameters, vocoder included: Generates 16 kHz mono speech with a compact acoustic model and FARGAN-derived vocoder. 🖥️ Built for local, offline use: Generate with either English voice, Pi or Vo, directly on CPU and begin playback in 10 ms audio frames after text encoding. 🗣️ Words come through clearly: PiVo reaches 0.532% WER on its held-out test set. The authors note that this measures intelligibility, not general English quality or naturalness. 🔤 Predictable English pronunciation: It expands numbers, currencies, ordinals, and ISO dates, while unsupported words raise a clear error instead of silently guessing. 📜 Code and acoustic weights are Apache-2.0. The FARGAN-derived vocoder uses Xiph/Opus BSD terms, and the pronunciation dictionary retains its CMU license.

We just released TensorRT Model Connect in Public Preview. You can take a supported @huggingface model to end-to-end TensorRT inference in just two commands. No intermediate ONNX export, and the resulting bundle can run through native C++ APIs. We also built the entire project with @OpenAIDevs Codex agents, with humans directing and reviewing the work. That includes model implementations, performance tuning, tests, integrations, and docs. It’s open source, so go try it out, dig into the implementations, or contribute support for a new model:

For five years, a little dog waited for the mail carrier every single day, holding a tennis ball. One day, he didn’t show up—he had passed away due to old age. His owner gave the ball to the mail carrier as a keepsake, and the mail carrier couldn’t hold back his tears.

Hy-MT2 keeps gaining momentum. Since its open-source release in May: → 700K+ downloads 🌟 → Hy-MT2-1.8B reached #1# on the Hugging Face trending, with 30B-A3B reaching #4# 🥇 → 70+ verified product and project integrations 💻 → Broader Hy-MT ecosystem support across Apple MLX-LM, Microsoft ONNX Runtime, NVIDIA NeMo, LLaMA-Factory, and more 👯 → Real-world adoption, including real-time multilingual translation of livestream comments on Bilibili 📺 And now, Hy-MT2-30B-A3B is officially available in GGUF format—addressing one of the community’s most-requested deployment needs and making local inference easier. Ready to run Hy-MT2-30B-A3B locally? Try the new GGUF release: Explore Hy-MT2: HuggingFace： Modelscope： Github： #TencentHy# #HyMT2# #OpenSource#