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Meta / Llama Mới nhất

Các mô hình Llama và Muse, phòng thí nghiệm siêu trí tuệ và các sản phẩm Meta AI của Meta.

8 tin chọn5 trong 30 ngày quaĐã thu thập 33

Cập nhật 9 tháng 11 00:00

Meta / Llama

9 tháng 11Thứ HaiMục 1–8 trong 8
  1. Meta AI Research Blog78

    Meta releases Muse Ember, its smallest and most efficient model for agent tasks

    Meta released Muse Ember, its smallest and most efficient model, aimed at the repetitive work agents do all day such as tool calls, coding subtasks, routing, extraction and long document parsing. Meta says it matches Claude Haiku 5.5's best score at about a third of the cost and beats every GPT-6 Luna setting, at $0.10 per million input tokens and $0.50 per million output tokens. It ships with a 1M-token context window, up to 512K output tokens, native text, image, audio and video input, and is available today on Muse Code and the Meta Model API as muse-ember-1.0.

    Vì sao đáng chú ý: Meta's smallest model targets the high-volume agent steps, and its cost-per-task comparison against Claude Haiku 5.5 and GPT-6 Luna shows where the efficiency claim comes from.

6 tháng 11Thứ Sáu
  1. Meta Newsroom · AI62

    Meta rolls out real-time voice and expressive avatars in its Muse app

    Meta is rolling out real-time voice conversations and expressive avatars in the Muse app on mobile, web and Mac, starting in the US. Users tap the phone icon to talk, can interrupt responses, and choose from hundreds of voices plus voice-only, avatar or full-screen modes. Voice is coming soon to AI Glasses and Muse Charm.

    Vì sao đáng chú ý: Meta's Muse app now supports real-time voice conversations with expressive avatars, showing how voice and interruption handling are moving into consumer AI assistants.

  2. Meta AI Research Blog60

    Meta brings specialized Muse and Meta AI models with Active Memory and Active Focus

    Meta is rolling out specialized versions of its existing models, trained using Muse Wonder, to people who use Muse and Meta AI. The specialized models add Active Memory and Active Focus, and Meta reports the current Muse model scores 47% on its Personal Superintelligence Index versus 38% for the previous model, 90% versus 76% on the Active Memory eval, and a 67% improvement in completing everyday tasks where unexpected issues arise.

    Vì sao đáng chú ý: Meta's specialized Muse and Meta AI models bring Active Memory and Active Focus to more users, with reported gains on memory and task-completion evals.

15 tháng 10Thứ Năm
  1. Meta AI Research Blog88

    Meta releases Muse Spark Open, an 816B open-weight multimodal model that runs on two Mac Studios

    Meta released the weights for Muse Spark Open, an open-weight natively multimodal model with 816B total parameters (42B active) and a 1M-token context window, positioned for coding and agentic work. It is the first model in its class to run on a desktop using two Apple M5 Ultra Mac Studios with the MLX framework, and it is available through API partners including Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI, with weights on Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden and Microsoft Foundry Models. Meta says the model ships with auto-tunable Helion GPU kernels in SGLang, delivering 1.03-3.95x kernel-level speedups on NVIDIA and AMD GPUs and a 3% end-to-end speedup on AMD MI350.

    Vì sao đáng chú ý: Meta releases open weights for an 816B-parameter multimodal model that runs on two Mac Studios, giving developers a local option for long-horizon coding and agentic tasks.

  2. Modal Blog78

    Meta's Muse Spark Open weights released and available on Modal Auto Endpoints

    Meta released the weights for Muse Spark Open, and Modal now serves the model through its Auto Endpoints with per-token billing. The model targets agentic coding and long-horizon tasks, with native multimodal understanding, a 1M-token context window, multi-step planning, sequential tool use and failure recovery. Meta reports 76.4 on Terminal-Bench 2.1 with the Terminus 2 agent and 60 on DeepSWE 1.1 using its preferred harness at xhigh reasoning effort.

    Vì sao đáng chú ý: Meta's open-weight Muse Spark Open is now served on Modal, giving teams a per-token way to try an agentic coding model with a 1M-token context window.

12 tháng 10Thứ Hai
  1. Meta AI Research Blog82

    Meta releases Muse Wonder and Muse Wonder Full frontier models for personal and professional agents

    Meta introduced the Muse Wonder series, comprising Muse Wonder and Muse Wonder Full, which it says delivers state-of-the-art performance on personal and professional agentic tasks, memory and coding. Muse Wonder is available today through the Meta Model API and Muse Code, while Muse Wonder Full is available today for Muse Code subscribers. The models pair Active Memory, which carries context across conversations and retrieves task-relevant details, with Active Focus, which sustains work on long-running goals and checks in before important actions; Meta says both were trained at its gigawatt-scale Prometheus data center.

    Vì sao đáng chú ý: Meta's Muse Wonder series claims state-of-the-art results on agentic, memory and coding benchmarks, with Active Memory and Active Focus as the two capabilities it is built around.

2 tháng 10Thứ Sáu
  1. Meta AI Research Blog71

    Meta shares six math papers co-authored with mathematicians and Muse Spark

    Meta published six mathematics papers produced in collaboration with mathematicians who used Muse Spark 1.1 and 1.2 in Thinking Mode through the regular meta.ai chat interface, with no custom research scaffold. Five of the papers answer previously open questions, covering Gaussian ellipsoid fitting thresholds, finite-time blow-up for a biharmonic nonlinear Schrödinger equation, a 384-element counterexample to a group-theory conjecture, tightness of a cycle-based relaxation in binary polynomial optimization, and a link between string two-point functions and height functions on curves. Each paper marks which passages were primarily drafted by researchers and which by AI, credits prior work, and acknowledges independent concurrent solutions from other teams.

    Vì sao đáng chú ý: Meta 公布六篇由数学家与 Muse Spark 协作完成的论文，其中五篇给出此前开放问题的答案，并标注 AI 与人类各自撰写的段落。

21 tháng 7, 2023Thứ Sáu
  1. Cursor Changelog62

    Cursor analyzes Llama-2-70B inference cost and latency against GPT-3.5

    Cursor measured Llama-2-70B inference on 2 80-GB A100 GPUs and found it over 3x cheaper than GPT-3.5 for prompt tokens but far more expensive for generated tokens at small batch sizes. Prompt processing costs about $0.00042 per 1K tokens versus GPT-3.5's $0.0015, while generation reaches $0.066 per 1K tokens at batch size 1 and only approaches GPT-3.5 pricing at large batches that push time to first token to several seconds. Cursor recommends open-source models for prompt-heavy tasks like classification and reranking, and closed-source APIs for generation-heavy workloads.

    Vì sao đáng chú ý: Cursor's measured cost and latency data shows when self-hosting Llama-2-70B beats GPT-3.5 and when it does not, offering a reusable framework for open-source model serving decisions.