←Search results

Search “Hugging Face”

By relevance · Total 1205 results Clear search

Tuesday

  6. Ant inclusionAI: New GitHub Repository

    Ant inclusionAI Open-Sources Ming-3.0-asr-tiny: Supports Chinese-English Transcription, Dialect Recognition, and Context ASR

    Ant inclusionAI has open-sourced Ming-3.0-asr-tiny on GitHub, combining the Ling-tiny-v3 language model with an audio encoder to support Chinese-English transcription, dialect recognition, and Context ASR. Dialect evaluation covers 15 test sets including Hunan, Minnan, Cantonese, and Sichuan-Chongqing; Context ASR can recognize proper nouns via hotwords in prompts or preceding dialogue. The inference stack uses Transformers 5.12.1, fla-core 0.5.2, and PyTorch 2.11.0, and provides an SGLang accelerated deployment example.

    Ming-3.0-asr-tiny model architecture

    Ant inclusionAI has open-sourced Ming-3.0-asr-tiny on GitHub, combining the Ling-tiny-v3 language model with an audio encoder to support Chinese-English transcription, dialect recognition, and Context ASR. Dialect evaluation covers 15 test sets including Hunan, Minnan, Cantonese, and Sichuan-Chongqing; Context ASR can recognize proper nouns via hotwords in prompts or preceding dialogue. The inference stack uses Transformers 5.12.1, fla-core 0.5.2, and PyTorch 2.11.0, and provides an SGLang accel

    Matched fieldsBody

    #engineering#English text is being prepared.#Speech#Open-source ecosystem
  8. Ant Group's inclusionAI: New GitHub repository

    Ant Group's inclusionAI open-sources the Ming-3.0-audio-flash native audio model

    Ant Group's inclusionAI has open-sourced the Ming-3.0-audio-flash native audio model on GitHub. It uses a 126B-parameter MoE architecture with 6.5B activated parameters, and there is also a Ming-3.0-audio-tiny version with 9.3B parameters and 2.7B activated parameters. The models cover Chinese and English recognition across multiple dialects and accents, long audio understanding, audio reasoning, and end-to-end speech dialogue. They support three inference methods: vLLM-Omni, SGLang, and Transformers, and are released under the MIT license.

    License: MIT

    Ant Group's inclusionAI has open-sourced the Ming-3.0-audio-flash native audio model on GitHub. It uses a 126B-parameter MoE architecture with 6.5B activated parameters, and there is also a Ming-3.0-audio-tiny version with 9.3B parameters and 2.7B activated parameters. The models cover Chinese and English recognition across multiple dialects and accents, long audio understanding, audio reasoning, and end-to-end speech dialogue. They support three inference methods: vLLM-Omni, SGLang, and Transfo

    Matched fieldsBody

    #engineering#Model releases#Open-source ecosystem#Speech
  11. AWS Machine Learning

    Amazon SageMaker JumpStart 2026 Progress Review

    From January to September 2026, Amazon SageMaker JumpStart added more than 100 models from 30 providers, of which Alibaba (Qwen) contributed 29, NVIDIA 17, Mistral 7, Google 6 Gemma 4 variants, IBM 4, and Cohere 4. The new models cover multimodal, speech, image, and video generation, and introduce quantized and compact models such as GLM-5.2 FP8 and JetBrains Mellum2-12B to reduce inference costs.

    Matched fieldsBody

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

Monday

  2. TechCrunch AI

    Laplace Intelligence conducts pre-deployment red team testing for robots and autonomous vehicles

    Laplace Intelligence, incubated at the University of Michigan, is building automated red team testing for robots and physical AI. It uses high-fidelity simulation environments post-trained on Nvidia Cosmos plus AI agents to automatically design corner cases, finding and fixing problems before deployment. The company has already generated revenue and is slightly profitable; its customers include autonomous trucking company Bot Auto and a major Japanese automaker, and it is running a free smart city pilot with Nvidia and T-Mobile in San Jose, California.

    Laplace Intelligence, incubated at the University of Michigan, is building automated red team testing for robots and physical AI. It uses high-fidelity simulation environments post-trained on Nvidia Cosmos plus AI agents to automatically design corner cases, finding and fixing problems before deployment. The company has already generated revenue and is slightly profitable; its customers include autonomous trucking company Bot Auto and a major Japanese automaker, and it is running a free smart ci

    Matched fieldsBody

    #tech-media#Industry news#Safety / alignment#Embodied AI
  5. n8n Blog

    How to run n8n AI workflows on NVIDIA DGX Spark without calling cloud APIs

    n8n Blog published a tutorial on how to deploy a complete model stack locally on a single NVIDIA DGX Spark, enabling n8n workflows to perform transcription, OCR, document Q&A, and image generation without calling cloud APIs. The entire stack exposes an OpenAI-compatible endpoint through LiteLLM; the main model is the NVFP4 version of Qwen3.6-35B-A3B, with a single-user generation speed of 120 tokens/s and an aggregate of 359 tokens/s across eight users. The author's testing found that image generation reduces chat speed to about one-third, and recommends that small teams queue image tasks or schedule them outside working hours.

    n8n Blog published a tutorial on how to deploy a complete model stack locally on a single NVIDIA DGX Spark, enabling n8n workflows to perform transcription, OCR, document Q&A, and image generation without calling cloud APIs. The entire stack exposes an OpenAI-compatible endpoint through LiteLLM; the main model is the NVFP4 version of Qwen3.6-35B-A3B, with a single-user generation speed of 120 tokens/s and an aggregate of 359 tokens/s across eight users. The author's testing found that image gene

    Matched fieldsBody

    #engineering#Tutorials / practice#English text is being prepared.#Open-source ecosystem