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Meta / Llama Latest news

Follow Meta AI, including Llama and Muse models, its superintelligence labs, and Meta AI products.

5 picksPast 30 days: 1 itemTotal: 80 items

Updated

Key moments

Since 2004
  1. CompanyFacebook founded
  2. CompanyFacebook AI Research founded with Yann LeCun
  3. ProductPyTorch released as open source
  4. CompanyFacebook renames itself Meta
  5. ModelLLaMA released to researchers
  6. ResearchSegment Anything released
  7. ModelLlama 2 released for commercial use
  8. ModelCode Llama released
  9. ModelLlama 3 released and Meta AI expands
  10. ModelLlama 3.1 405B released
  11. ModelLlama 3.2 adds vision and small models
  12. ModelLlama 4 Scout and Maverick released
  13. CompanyForms Meta Superintelligence Labs after investing in Scale AI
  14. ModelMuse Spark 1.1 released
  15. ModelMeta launches Muse Image, an agentic image model with search and code tools
  16. ModelMuse Spark 1.3 released

Meta / Llama top picks

Sep 21MonItems 1–5
  1. Tencent Hunyuan67

    Tencent Hy4 preview compressed to 214 GiB with mixed-precision quantization

    Tencent Hunyuan says it shrank the 770B-parameter Hy4 preview from roughly 1.5TB to 214 GiB while keeping the parameter count unchanged. The quoted Zhihu post by a Tencent Hunyuan quantization team member describes the method: a 1.25-bit sparse ternary encoding, mixed precision across expert layers, and STQ1_0 CUDA kernels in llama.cpp. The author reports nearly unchanged MRCR retrieval and a small decline in math.

    Why it matters: The quoted Zhihu post explains how Hy4 preview's weights were quantized and kept usable at inference, a concrete engineering case for compressing large MoE models.

Sep 8Tue
  1. AI at Meta67

    Meta introduces Muse, a personal agent powered by Muse Spark 1.3

    Meta announced Muse, a personal AI agent designed to get things done for users across many parts of life. The product is powered by Muse Spark 1.3, and the post links to an app download and a page describing how Muse was built.

    Why it matters: The announcement names Muse Spark 1.3 as the underlying model, giving readers a concrete product and model pairing to track.

Jul 9Thu
  1. Meta AI Blog72

    Meta releases Muse Spark 1.1 with agent and coding gains

    Meta Superintelligence Labs has introduced Muse Spark 1.1, a multimodal reasoning model aimed at agentic tasks, with gains in tool use, computer use, coding, and multimodal understanding. It supports a 1 million token context window and is available in Thinking mode in the Meta AI app and on meta.ai, with developers able to access it through a public preview of the Meta Model API.

    Why it matters: The post specifies Muse Spark 1.1's agent, coding, and multimodal gains and its Meta Model API preview access, which helps developers judge its fit for their workflows.

Jul 7Tue
  1. Meta AI Blog75

    Meta launches Muse Image, an agentic image model with search and code tools

    Meta Superintelligence Labs has released Muse Image, which can invoke search and coding tools and self-refine its generations before output. It is available today in the Meta AI app, meta.ai, Instagram Stories in the US, and WhatsApp in limited countries, with Facebook coming soon. Meta also previewed Muse Video, which is coming soon to creators and Meta AI and is reported as ranking No. 3 on Arena for text-to-video at the time of writing.

    Why it matters: The source describes how search, code execution, and self-refinement change image generation, which matters to anyone comparing agentic media models with plain prompt-to-image systems.

Jun 29Mon
  1. Meta AI Blog68

    Meta's Brain2Qwerty v2 decodes sentences from non-invasive brain recordings

    Meta released Brain2Qwerty v2, an end-to-end deep learning pipeline that decodes sentences in real time from non-invasive brain recordings. The model reached 61% word accuracy across participants, compared with 8% for other non-invasive methods, and 78% for the best participant. Meta also released the v1 and v2 training code, and partner BCBL released the v1 dataset.

    Why it matters: The source reports word accuracy and data-scaling results for non-invasive decoding, offering a benchmark against surgical brain-computer interfaces and prior non-invasive methods.