NAll news

Tuesday89 items

  1. AntLingAGIProduct release
    AI score58

    AntLing Ming-3.0 Speech Models Integrated into atomic.chat Local Workflow

    AntLingAGI thanks atomic.chat for integrating Ming-3.0 into local AI workflows, making open-source speech models more accessible. According to atomic.chat, Ming-3.0 comprises 5 open-source models covering speaking, listening, and speech transcription; its speech model ranks No. 1 among open-source models on Artificial Analysis, and the smallest version is only 0.5B.

    Run @AntLingAGI speech models locally 🎙️ Ming-3.0 is 5 open models that talk, listen and write down what you say Its voice model now ranks #1 among open models on Artificial Analysis starting at just 0.5B Run AI models locally -> https://t.co/RbcCOIgVkj https://t.co/vbqafAAd2x

    Same story · 2 reports

    #Product updates#Open-source ecosystem#Speech

  2. Ant LingProduct release
    AI score67

    Ant Ling's Ming-3.0 Speech Models Get Day-0 Support in vLLM-Omni

    Ant Ling has open-sourced 5 speech models covering TTS, audio understanding, and ASR, with total parameter counts ranging from 0.5B to 126B (6.5B active parameters). vLLM announced that vLLM-Omni provided Ming-3.0 support on the day of release; Ant Ling thanked them for this and said deployment is now more convenient.

    Day-0 support for Ming-3.0 in vLLM-Omni @AntLingAGI just open-sourced 5 speech models spanning TTS, audio understanding, and ASR — ranging from 0.5B to 126B total parameters (6.5B active). Serve them today with vLLM-Omni. Congrats to the @AntLingAGI team!

    Same story · 2 reports

    #Product updates#English text is being prepared.#Open-source ecosystem

  3. openbmbProduct release
    AI score53

    English translation is being prepared.

    MiniCPM-V 4.7 running locally at 100 tokens/second! New multimodal models from @OpenBMB using only ~3 GB of memory. Below, I showcase how to run it with @llama_cpp's web UI 🧵 https://t.co/tItzIAD77q

    #Product updates#English text is being prepared.#Multimodal

  4. Hugging FaceFirst-partyProduct release
    AI score60

    Hugging Face launches TorchNeuron, enabling HF models to run natively on AWS Trainium

    Hugging Face has released the PyTorch plugin TorchNeuron, which turns Neuron into a native device, supporting eager mode, torch.compile, and distributed interfaces such as FSDP2 and DTensor. It is available as a public beta starting October 20, 2026, and can be installed via pip install torch-neuronx. Transformers, Diffusers, Accelerate, PEFT, and TRL can run directly on Trainium without going through Optimum Neuron; the official recommendation is for Neuron users to switch to these libraries. For text generation, 89% of the top 100 most commonly used architectures are supported out of the box, compared with 46% for Optimum Neuron v0.4.5, and all that is needed is to pass device_map=neuron.

    Same story · 4 reports

    #Product updates#English text is being prepared.#Open-source ecosystem